Coronavirus US Data
davegoblue
10/1/2020
library(tidyverse)## -- Attaching packages --------------------------------------------------------------------------- tidyverse 1.3.0 --## v ggplot2 3.2.1 v purrr 0.3.3
## v tibble 2.1.3 v dplyr 0.8.4
## v tidyr 1.0.2 v stringr 1.4.0
## v readr 1.3.1 v forcats 0.4.0## -- Conflicts ------------------------------------------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()Background
This file is for analysis of several coronavirus data sources focused on the US in 2020. Data include areas such as cases, hospitalizations, and deaths tracked to cornavirus, as well as all-cause deaths.
The previous version of this file includes many exploratory analysis components. This version is designed with the following portions of the previous code:
- Functions
- Run key analyses
- Save and load key files
COVID Tracking Project
The first section is for analysis of data from The COVID Tracking Project. This file contains data on positive tests, hospitalizations, deaths, and the like for coronavirus cases in the US. Downloaded data are unique by state and date.
Key Functions (COVID Tracking Project, USA Facts, CDC All-Cause Deaths)
Functions for working with data from the COVID Tracking Project, USA Facts, and the CDC All-Cause Death data are available in a separate file which is sourced below:
source("./Coronavirus_Statistics_Functions_v002.R")Running Key Analyses (COVID Tracking Project)
The data from COVID Tracking Project can be loaded and analyzed using the functions above and a variable mapping file:
# STEP 0: Create a variable mapping file
varMapper <- c("cases"="Cases",
"newCases"="Increase in cases, most recent 30 days",
"casesroll7"="Rolling 7-day mean cases",
"deaths"="Deaths",
"newDeaths"="Increase in deaths, most recent 30 days",
"deathsroll7"="Rolling 7-day mean deaths",
"cpm"="Cases per million",
"cpm7"="Cases per day (7-day rolling mean) per million",
"newcpm"="Increase in cases, most recent 30 days, per million",
"dpm"="Deaths per million",
"dpm7"="Deaths per day (7-day rolling mean) per million",
"newdpm"="Increase in deaths, most recent 30 days, per million",
"hpm7"="Currently Hospitalized per million (7-day rolling mean)",
"tpm"="Tests per million",
"tpm7"="Tests per million per day (7-day rolling mean)"
)
# Test function for hierarchical clustering with Vermont reassigned to New Hampshire
test_hier5 <- readRunCOVIDTrackingProject(thruLabel="Aug 20, 2020",
readFrom="./RInputFiles/Coronavirus/CV_downloaded_200820.csv",
hierarchical=TRUE,
kCut=6,
reAssignState=list("VT"="NH"),
minShape=3,
ratioDeathvsCase = 5,
ratioTotalvsShape = 0.5,
minDeath=100,
minCase=10000
)## Parsed with column specification:
## cols(
## .default = col_double(),
## state = col_character(),
## dataQualityGrade = col_character(),
## lastUpdateEt = col_character(),
## dateModified = col_datetime(format = ""),
## checkTimeEt = col_character(),
## dateChecked = col_datetime(format = ""),
## hash = col_character(),
## grade = col_logical()
## )## See spec(...) for full column specifications.##
## File is unique by state and date
##
##
## Overall control totals in file:
## # A tibble: 1 x 3
## positiveIncrease deathIncrease hospitalizedCurrently
## <dbl> <dbl> <dbl>
## 1 5546056 166127 6486294
## Observations: 9,449
## Variables: 53
## $ date <date> 2020-08-20, 2020-08-20, 2020-08-20, 20...
## $ state <chr> "AK", "AL", "AR", "AS", "AZ", "CA", "CO...
## $ positive <dbl> 5332, 112449, 54765, 0, 196280, 644751,...
## $ negative <dbl> 307315, 784330, 593744, 1514, 922163, 9...
## $ pending <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
## $ hospitalizedCurrently <dbl> 51, 1105, 499, NA, 1070, 6212, 238, 47,...
## $ hospitalizedCumulative <dbl> NA, 13330, 3790, NA, 21143, NA, 6784, 1...
## $ inIcuCurrently <dbl> NA, NA, NA, NA, 388, 1707, NA, NA, 26, ...
## $ inIcuCumulative <dbl> NA, 1348, NA, NA, NA, NA, NA, NA, NA, N...
## $ onVentilatorCurrently <dbl> 6, NA, 108, NA, 233, NA, NA, NA, 12, NA...
## $ onVentilatorCumulative <dbl> NA, 734, 488, NA, NA, NA, NA, NA, NA, N...
## $ recovered <dbl> 1513, 44684, 48458, NA, 28471, NA, 5759...
## $ dataQualityGrade <chr> "A", "B", "A", "C", "A+", "B", "A", "B"...
## $ lastUpdateEt <chr> "8/20/2020 0:00", "8/20/2020 11:00", "8...
## $ dateModified <dttm> 2020-08-20 00:00:00, 2020-08-20 11:00:...
## $ checkTimeEt <chr> "8/19/2020 20:00", "8/20/2020 7:00", "8...
## $ death <dbl> 29, 1974, 641, 0, 4684, 11686, 1800, 44...
## $ hospitalized <dbl> NA, 13330, 3790, NA, 21143, NA, 6784, 1...
## $ dateChecked <dttm> 2020-08-20 00:00:00, 2020-08-20 11:00:...
## $ totalTestsViral <dbl> 312647, 891813, 648509, NA, 1116897, 10...
## $ positiveTestsViral <dbl> 4970, NA, NA, NA, NA, NA, NA, NA, NA, N...
## $ negativeTestsViral <dbl> 307360, NA, 593744, NA, NA, NA, NA, NA,...
## $ positiveCasesViral <dbl> 5332, 107483, 54765, 0, 194734, 644751,...
## $ deathConfirmed <dbl> 29, 1905, NA, NA, 4429, NA, NA, 3572, N...
## $ deathProbable <dbl> NA, 69, NA, NA, 255, NA, NA, 886, NA, 6...
## $ totalTestEncountersViral <dbl> NA, NA, NA, NA, NA, NA, 895207, NA, NA,...
## $ totalTestsPeopleViral <dbl> NA, NA, NA, 1514, NA, NA, 645170, NA, 2...
## $ totalTestsAntibody <dbl> NA, NA, NA, NA, 255456, NA, 150931, NA,...
## $ positiveTestsAntibody <dbl> NA, NA, NA, NA, NA, NA, 10406, NA, NA, ...
## $ negativeTestsAntibody <dbl> NA, NA, NA, NA, NA, NA, 140525, NA, NA,...
## $ totalTestsPeopleAntibody <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
## $ positiveTestsPeopleAntibody <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
## $ negativeTestsPeopleAntibody <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
## $ totalTestsPeopleAntigen <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
## $ positiveTestsPeopleAntigen <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
## $ totalTestsAntigen <dbl> NA, NA, 10358, NA, NA, NA, NA, NA, NA, ...
## $ positiveTestsAntigen <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
## $ fips <dbl> 2, 1, 5, 60, 4, 6, 8, 9, 11, 10, 12, 13...
## $ positiveIncrease <dbl> 85, 971, 549, 0, 723, 5920, 270, 118, 5...
## $ negativeIncrease <dbl> 1713, 10462, 6680, 0, 6481, 81363, 4657...
## $ total <dbl> 312647, 896779, 648509, 1514, 1118443, ...
## $ totalTestResults <dbl> 312647, 896779, 648509, 1514, 1118443, ...
## $ totalTestResultsIncrease <dbl> 1798, 11433, 7229, 0, 7204, 87283, 7348...
## $ posNeg <dbl> 312647, 896779, 648509, 1514, 1118443, ...
## $ deathIncrease <dbl> 0, 30, 10, 0, 50, 163, 12, 1, 1, 0, 119...
## $ hospitalizedIncrease <dbl> 0, 250, 47, 0, 123, 0, 3, 72, 0, 0, 450...
## $ hash <chr> "c83a1d575a597788adccbe170950b8d197754b...
## $ commercialScore <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ negativeRegularScore <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ negativeScore <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ positiveScore <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ score <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ grade <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
##
##
## Control totals - note that validState other than TRUE will be discarded
##
## # A tibble: 2 x 6
## validState cases deaths hosp tests n
## <lgl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 FALSE 29761 385 NA 392743 790
## 2 TRUE 5516295 165742 NA 69638073 8659
## Observations: 8,659
## Variables: 6
## $ date <date> 2020-08-20, 2020-08-20, 2020-08-20, 2020-08-20, 2020-08-20,...
## $ state <chr> "AK", "AL", "AR", "AZ", "CA", "CO", "CT", "DC", "DE", "FL", ...
## $ cases <dbl> 85, 971, 549, 723, 5920, 270, 118, 55, 75, 4555, 2759, 235, ...
## $ deaths <dbl> 0, 30, 10, 50, 163, 12, 1, 1, 0, 119, 55, 3, 8, 9, 27, 11, 0...
## $ hosp <dbl> 51, 1105, 499, 1070, 6212, 238, 47, 78, 40, 5067, 2506, 187,...
## $ tests <dbl> 1798, 11433, 7229, 7204, 87283, 7348, 12420, 2326, 2087, 295...
## Observations: 8,659
## Variables: 14
## $ date <date> 2020-01-22, 2020-01-23, 2020-01-24, 2020-01-25, 2020-01-26,...
## $ state <chr> "WA", "WA", "WA", "WA", "WA", "WA", "WA", "WA", "WA", "WA", ...
## $ cases <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 2, 1, 1, 0, 3, 1, 1, 0, 0, ...
## $ deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ hosp <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, ...
## $ tests <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 2, 1, 1, 0, 3, 1, 1, 0, 0, ...
## $ cpm <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.000...
## $ dpm <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ hpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, ...
## $ tpm <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.000...
## $ cpm7 <dbl> NA, NA, NA, 0.00000000, 0.01992331, 0.01992331, 0.01992331, ...
## $ dpm7 <dbl> NA, NA, NA, 0.00000000, 0.00000000, 0.00000000, 0.00000000, ...
## $ hpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, ...
## $ tpm7 <dbl> NA, NA, NA, 0.00000000, 0.01992331, 0.01992331, 0.01992331, ...
##
## Recency is defined as 2020-07-22 through current
##
## Recency is defined as 2020-07-22 through current
# Test function for k-means clustering using the per capita data file previously created
test_km5 <- readRunCOVIDTrackingProject(thruLabel="Aug 20, 2020",
dfPerCapita=test_hier5$dfPerCapita,
hierarchical=FALSE,
makeCumulativePlots=FALSE,
minShape=3,
ratioDeathvsCase = 5,
ratioTotalvsShape = 0.5,
minDeath=100,
minCase=10000,
nCenters=5,
testCenters=1:10,
iter.max=20,
nstart=10,
seed=2008261400
)
##
## Cluster means and counts
## 1 2 3 4 5
## . 10.00 14.00 9.00 14.00 4.00
## totalCases 0.28 0.86 0.71 0.50 0.78
## totalDeaths 0.41 1.42 2.73 0.99 5.32
## cases_3 0.03 0.01 0.05 0.02 0.10
## deaths_3 0.27 0.08 0.13 0.06 0.15
## cases_4 0.07 0.06 0.26 0.12 0.50
## deaths_4 1.27 0.63 1.73 1.00 2.58
## cases_5 0.06 0.07 0.24 0.19 0.23
## deaths_5 0.69 0.66 1.77 1.50 1.59
## cases_6 0.08 0.18 0.11 0.15 0.07
## deaths_6 0.42 0.61 0.70 1.01 0.44
## cases_7 0.23 0.44 0.21 0.28 0.06
## deaths_7 0.70 1.60 0.42 0.81 0.18
## cases_8 0.16 0.22 0.14 0.19 0.04
## deaths_8 0.70 1.38 0.25 0.61 0.06
##
## Recency is defined as 2020-07-22 through current
##
## Recency is defined as 2020-07-22 through current
# Run in a session that has access to the old data files
# identical(test_hier5$useClusters, clustVec) # TRUE
# identical(test_km5$useClusters, testCluster_km5$objCluster$cluster) # TRUECluster files produced using the new functions and the existing August 20, 2020 data are identical to ‘clustVec’ and ‘testCluster_km5’ produced in the _v001 code.
Further, new data is downloaded that is current through September 30, 2020, and the test_hier5 clusters are used for analysis:
# Test function for hierarchical clustering with Vermont reassigned to New Hampshire
locDownload <- "./RInputFiles/Coronavirus/CV_downloaded_201001.csv"
test_hier5_201001 <- readRunCOVIDTrackingProject(thruLabel="Sep 30, 2020",
readFrom=locDownload,
compareFile=test_hier5$dfRaw,
useClusters=test_hier5$useClusters
)## Parsed with column specification:
## cols(
## .default = col_double(),
## state = col_character(),
## dataQualityGrade = col_character(),
## lastUpdateEt = col_character(),
## dateModified = col_datetime(format = ""),
## checkTimeEt = col_character(),
## dateChecked = col_datetime(format = ""),
## fips = col_character(),
## totalTestResultsSource = col_character(),
## hash = col_character(),
## grade = col_logical()
## )## See spec(...) for full column specifications.##
## File is unique by state and date
##
##
## Overall control totals in file:
## # A tibble: 1 x 3
## positiveIncrease deathIncrease hospitalizedCurrently
## <dbl> <dbl> <dbl>
## 1 7198509 198929 7830770
##
## *** COMPARISONS TO REFERENCE FILE: compareFile
##
## Checkin for similarity of: column names
## In reference but not in current:
## In current but not in reference: totalTestResultsSource
##
## Checkin for similarity of: states
## In reference but not in current:
## In current but not in reference:
##
## Checkin for similarity of: dates
## In reference but not in current:
## In current but not in reference: 2020-09-30 2020-09-29 2020-09-28 2020-09-27 2020-09-26 2020-09-25 2020-09-24 2020-09-23 2020-09-22 2020-09-21 2020-09-20 2020-09-19 2020-09-18 2020-09-17 2020-09-16 2020-09-15 2020-09-14 2020-09-13 2020-09-12 2020-09-11 2020-09-10 2020-09-09 2020-09-08 2020-09-07 2020-09-06 2020-09-05 2020-09-04 2020-09-03 2020-09-02 2020-09-01 2020-08-31 2020-08-30 2020-08-29 2020-08-28 2020-08-27 2020-08-26 2020-08-25 2020-08-24 2020-08-23 2020-08-22 2020-08-21
##
## *** Difference of at least 5 and difference is at least 1%:## Joining, by = c("date", "name")## date name newValue oldValue
## 1 2020-02-15 positiveIncrease 0 7
## 2 2020-02-16 positiveIncrease 0 7
## 3 2020-02-17 positiveIncrease 0 15
## 4 2020-02-18 positiveIncrease 0 9
## 5 2020-02-19 positiveIncrease 0 10
## 6 2020-02-20 positiveIncrease 0 13
## 7 2020-02-21 positiveIncrease 0 11
## 8 2020-02-22 positiveIncrease 0 13
## 9 2020-02-23 positiveIncrease 0 16
## 10 2020-02-24 positiveIncrease 0 26
## 11 2020-02-25 positiveIncrease 0 31
## 12 2020-02-26 positiveIncrease 0 29
## 13 2020-02-27 positiveIncrease 0 27
## 14 2020-02-28 positiveIncrease 0 40
## 15 2020-02-29 positiveIncrease 18 24
## 16 2020-03-01 positiveIncrease 16 78
## 17 2020-03-02 positiveIncrease 44 82
## 18 2020-03-03 positiveIncrease 48 100
## 19 2020-03-04 positiveIncrease 62 110
## 20 2020-03-05 positiveIncrease 103 151
## 21 2020-03-06 positiveIncrease 108 137
## 22 2020-03-07 positiveIncrease 175 217
## 23 2020-03-08 positiveIncrease 198 267
## 24 2020-03-09 positiveIncrease 292 367
## 25 2020-03-10 positiveIncrease 387 441
## 26 2020-03-11 positiveIncrease 509 527
## 27 2020-03-13 positiveIncrease 1072 1025
## 28 2020-03-15 positiveIncrease 1291 1251
## 29 2020-03-16 positiveIncrease 1739 1560
## 30 2020-03-17 positiveIncrease 2588 3613
## 31 2020-03-18 positiveIncrease 3089 3171
## 32 2020-03-20 positiveIncrease 6147 6255
## 33 2020-03-21 positiveIncrease 6793 6885
## 34 2020-03-22 positiveIncrease 9125 9259
## 35 2020-03-24 positiveIncrease 10769 10632
## 36 2020-04-03 hospitalizedCurrently 25729 25472
## 37 2020-04-29 positiveIncrease 26168 26641
## 38 2020-04-30 positiveIncrease 29975 29568
## 39 2020-05-08 positiveIncrease 27222 27605
## 40 2020-05-09 positiveIncrease 25218 24810
## 41 2020-05-10 positiveIncrease 21055 21504
## 42 2020-05-12 positiveIncrease 22890 22663
## 43 2020-05-26 positiveIncrease 16825 16629
## 44 2020-06-08 positiveIncrease 17209 17012
## 45 2020-06-19 positiveIncrease 31471 31046
## 46 2020-06-21 positiveIncrease 27928 27284
## 47 2020-06-23 positiveIncrease 33447 33021
## 48 2020-06-24 positiveIncrease 39075 38684
## 49 2020-06-25 positiveIncrease 39637 39072
## 50 2020-06-27 positiveIncrease 43164 43783
## 51 2020-06-29 positiveIncrease 39813 39175
## 52 2020-07-26 positiveIncrease 61000 61713
## 53 2020-07-28 positiveIncrease 59003 56229
## 54 2020-07-29 positiveIncrease 64408 66969
## 55 2020-08-02 positiveIncrease 46812 48266
## 56 2020-08-09 positiveIncrease 50627 51365
## 57 2020-08-16 positiveIncrease 42487 43083
## 58 2020-08-20 positiveIncrease 43758 43245
## 59 2020-08-20 deathIncrease 1134 1117## Joining, by = c("date", "name")##
##
## *** Difference of at least 5 and difference is at least 1%:## Joining, by = c("state", "name")
## state name newValue oldValue
## 1 WA positiveIncrease 70973 68687
## 2 WI hospitalizedCurrently 45951 51401
## Observations: 11,802
## Variables: 54
## $ date <date> 2020-09-30, 2020-09-30, 2020-09-30, 20...
## $ state <chr> "AK", "AL", "AR", "AS", "AZ", "CA", "CO...
## $ positive <dbl> 8780, 154701, 83697, 0, 218507, 810625,...
## $ negative <dbl> 448427, 994475, 949107, 1571, 1243641, ...
## $ pending <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
## $ totalTestResults <dbl> 457207, 1132039, 1029717, 1571, 1457575...
## $ hospitalizedCurrently <dbl> 53, 776, 484, NA, 560, 3267, 268, 104, ...
## $ hospitalizedCumulative <dbl> NA, 17257, 5354, NA, 22119, NA, 7558, 1...
## $ inIcuCurrently <dbl> NA, NA, 218, NA, 115, 830, NA, NA, 28, ...
## $ inIcuCumulative <dbl> NA, 1815, NA, NA, NA, NA, NA, NA, NA, N...
## $ onVentilatorCurrently <dbl> 7, NA, 93, NA, 55, NA, NA, NA, 9, NA, N...
## $ onVentilatorCumulative <dbl> NA, 1025, 676, NA, NA, NA, NA, NA, NA, ...
## $ recovered <dbl> 4555, 67948, 75312, NA, 35261, NA, 6500...
## $ dataQualityGrade <chr> "A", "A", "A+", "D", "A+", "B", "A", "B...
## $ lastUpdateEt <chr> "9/30/2020 03:59", "9/30/2020 11:00", "...
## $ dateModified <dttm> 2020-09-30 03:59:00, 2020-09-30 11:00:...
## $ checkTimeEt <chr> "09/29 23:59", "09/30 07:00", "09/29 20...
## $ death <dbl> 56, 2540, 1369, 0, 5650, 15792, 1952, 4...
## $ hospitalized <dbl> NA, 17257, 5354, NA, 22119, NA, 7558, 1...
## $ dateChecked <dttm> 2020-09-30 03:59:00, 2020-09-30 11:00:...
## $ totalTestsViral <dbl> 457207, 1132039, 1029717, 1571, NA, 147...
## $ positiveTestsViral <dbl> 7807, NA, NA, NA, NA, NA, NA, NA, NA, N...
## $ negativeTestsViral <dbl> 449109, NA, 949107, NA, NA, NA, NA, NA,...
## $ positiveCasesViral <dbl> 8780, 137564, 80610, 0, 213934, 810625,...
## $ deathConfirmed <dbl> 56, 2399, 1223, NA, 5382, NA, NA, 3610,...
## $ deathProbable <dbl> NA, 141, 146, NA, 268, NA, NA, 898, NA,...
## $ totalTestEncountersViral <dbl> NA, NA, NA, NA, NA, NA, 1341180, NA, 38...
## $ totalTestsPeopleViral <dbl> NA, NA, NA, NA, 1457575, NA, 902242, NA...
## $ totalTestsAntibody <dbl> NA, NA, NA, NA, 293098, NA, 167989, NA,...
## $ positiveTestsAntibody <dbl> NA, NA, NA, NA, NA, NA, 11733, NA, NA, ...
## $ negativeTestsAntibody <dbl> NA, NA, NA, NA, NA, NA, 156256, NA, NA,...
## $ totalTestsPeopleAntibody <dbl> NA, 58393, NA, NA, NA, NA, NA, NA, NA, ...
## $ positiveTestsPeopleAntibody <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
## $ negativeTestsPeopleAntibody <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
## $ totalTestsPeopleAntigen <dbl> NA, NA, 11700, NA, NA, NA, NA, NA, NA, ...
## $ positiveTestsPeopleAntigen <dbl> NA, NA, 3383, NA, NA, NA, NA, NA, NA, N...
## $ totalTestsAntigen <dbl> NA, NA, 21856, NA, NA, NA, NA, NA, NA, ...
## $ positiveTestsAntigen <dbl> NA, NA, 3300, NA, NA, NA, NA, NA, NA, N...
## $ fips <chr> "02", "01", "05", "60", "04", "06", "08...
## $ positiveIncrease <dbl> 106, 1147, 942, 0, 323, 3200, 535, 221,...
## $ negativeIncrease <dbl> 6045, 11312, 21205, 0, 3735, 88457, 674...
## $ total <dbl> 457207, 1149176, 1032804, 1571, 1462148...
## $ totalTestResultsSource <chr> "posNeg", "posNeg", "posNeg", "posNeg",...
## $ totalTestResultsIncrease <dbl> 6151, 12327, 21812, 0, 4047, 91657, 145...
## $ posNeg <dbl> 457207, 1149176, 1032804, 1571, 1462148...
## $ deathIncrease <dbl> 0, 23, 19, 0, 18, 152, 7, 3, 1, 1, 175,...
## $ hospitalizedIncrease <dbl> 0, 75, 0, 0, 72, 0, 28, 0, 0, 0, 253, 1...
## $ hash <chr> "1716aba4b705ff74814829d25487c6e57d2712...
## $ commercialScore <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ negativeRegularScore <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ negativeScore <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ positiveScore <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ score <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ grade <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
##
##
## Control totals - note that validState other than TRUE will be discarded
##
## # A tibble: 2 x 6
## validState cases deaths hosp tests n
## <lgl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 FALSE 52622 732 NA 442114 995
## 2 TRUE 7145887 198197 NA 103496519 10807
## Observations: 10,807
## Variables: 6
## $ date <date> 2020-09-30, 2020-09-30, 2020-09-30, 2020-09-30, 2020-09-30,...
## $ state <chr> "AK", "AL", "AR", "AZ", "CA", "CO", "CT", "DC", "DE", "FL", ...
## $ cases <dbl> 106, 1147, 942, 323, 3200, 535, 221, 26, 82, 1948, 1720, 87,...
## $ deaths <dbl> 0, 23, 19, 18, 152, 7, 3, 1, 1, 175, 27, 0, 16, 4, 35, 20, 4...
## $ hosp <dbl> 53, 776, 484, 560, 3267, 268, 104, 95, 72, 2097, 1896, 147, ...
## $ tests <dbl> 6151, 12327, 21812, 4047, 91657, 14538, 12401, 1464, 1074, 1...
## Observations: 10,807
## Variables: 14
## $ date <date> 2020-01-22, 2020-01-22, 2020-01-23, 2020-01-23, 2020-01-24,...
## $ state <chr> "MA", "WA", "MA", "WA", "MA", "WA", "MA", "WA", "MA", "WA", ...
## $ cases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ hosp <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, ...
## $ tests <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, ...
## $ cpm <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ dpm <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ hpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, ...
## $ tpm <dbl> 0.0000000, 0.0000000, 0.1471796, 0.0000000, 0.0000000, 0.000...
## $ cpm7 <dbl> NA, NA, NA, NA, NA, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ dpm7 <dbl> NA, NA, NA, NA, NA, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ hpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, ...
## $ tpm7 <dbl> NA, NA, NA, NA, NA, NA, 0.04205130, 0.00000000, 0.06307695, ...
##
## Recency is defined as 2020-09-01 through current
##
## Recency is defined as 2020-09-01 through current
Segments appear to be on trend with previous analysis, with what was previously called “late pandemic” having peaked and the segments that had smaller deaths per million showing higher percentage increases.
USA Facts
The second section is for analysis of data from USA Facts. This site contains county-level data for the US focused on coronavirus cases and deaths. Data are provided with one row per county and one column for each date, and are thus unique by county. There are differences in state and US totals for coronavirus cases and deaths when reported by COVID Tracking Project and USA Facts, though trends, timing, and relative magnitudes are generally in agreement between the two sources.
Key Functions (USA Facts)
Functions for reading and analyzing data from USA Facts are sourced above from Coronavirus_Statistics_Functions_v002.R
Running Key Analyses (USA Facts)
The data from USA Facts can be loaded and analyzed using the sourced functions, a variable mapping file, and a main function that calls the other functions and returns a list:
# STEP 1a: Define the locations for the population, cases, and deaths file
popFile <- "./RInputFiles/Coronavirus/covid_county_population_usafacts.csv"
caseFile_20200903 <- "./RInputFiles/Coronavirus/covid_confirmed_usafacts_downloaded_20200903.csv"
deathFile_20200903 <- "./RInputFiles/Coronavirus/covid_deaths_usafacts_downloaded_20200903.csv"
maxDate_20200903 <- "2020-08-31"
# STEP 1b: Read and pivot data from USA Facts; extract population data file as pop_usafacts
rawUSAFacts_20200903 <- readPivotUSAFacts(popFile=popFile,
caseFile=caseFile_20200903,
deathFile=deathFile_20200903,
unassignedDate=maxDate_20200903
)## Parsed with column specification:
## cols(
## countyFIPS = col_double(),
## `County Name` = col_character(),
## State = col_character(),
## population = col_double()
## )## Parsed with column specification:
## cols(
## .default = col_double(),
## `County Name` = col_character(),
## State = col_character()
## )## See spec(...) for full column specifications.## Parsed with column specification:
## cols(
## .default = col_double(),
## `County Name` = col_character(),
## State = col_character()
## )## See spec(...) for full column specifications.## Joining, by = c("state", "date", "unassigned")
pop_usafacts <- rawUSAFacts_20200903$pop
# STEP 2: Read case and death data (redundant), combine, and add population totals; no clusters by default
burden_20200903_new <- readUSAFacts(
caseFile=caseFile_20200903,
deathFile=deathFile_20200903,
stateClusters=NULL
)## Parsed with column specification:
## cols(
## .default = col_double(),
## `County Name` = col_character(),
## State = col_character()
## )## See spec(...) for full column specifications.## Observations: 3,195
## Variables: 228
## $ countyFIPS <dbl> 0, 1001, 1003, 1005, 1007, 1009, 1011, 1013, 1015, 10...
## $ `County Name` <chr> "Statewide Unallocated", "Autauga County", "Baldwin C...
## $ State <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL",...
## $ stateFIPS <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ `1/22/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/23/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/24/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/25/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/26/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/27/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/28/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/29/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/30/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/31/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/1/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/2/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/3/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/4/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/5/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/6/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/7/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/8/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/9/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/10/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/11/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/12/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/13/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/14/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/15/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/16/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/17/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/18/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/19/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/20/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/21/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/22/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/23/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/24/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/25/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/26/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/27/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/28/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/29/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/1/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/2/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/3/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/4/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/5/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/6/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/7/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/8/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/9/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/10/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/11/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/12/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/13/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/14/20` <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/15/20` <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/16/20` <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/17/20` <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/18/20` <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/19/20` <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/20/20` <dbl> 0, 0, 2, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/21/20` <dbl> 0, 0, 2, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/22/20` <dbl> 0, 0, 3, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/23/20` <dbl> 0, 0, 3, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/24/20` <dbl> 0, 1, 4, 0, 0, 0, 0, 0, 2, 5, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/25/20` <dbl> 0, 4, 4, 0, 0, 1, 0, 1, 2, 10, 1, 1, 0, 0, 1, 1, 0, 1...
## $ `3/26/20` <dbl> 0, 6, 5, 0, 0, 2, 2, 1, 2, 13, 1, 4, 1, 0, 1, 1, 0, 1...
## $ `3/27/20` <dbl> 0, 6, 5, 0, 0, 5, 2, 1, 3, 15, 1, 7, 1, 0, 1, 3, 0, 1...
## $ `3/28/20` <dbl> 0, 6, 10, 0, 0, 5, 2, 1, 3, 17, 1, 7, 1, 0, 2, 4, 0, ...
## $ `3/29/20` <dbl> 0, 6, 15, 0, 0, 5, 2, 1, 3, 27, 2, 8, 1, 0, 2, 5, 0, ...
## $ `3/30/20` <dbl> 0, 7, 18, 0, 2, 5, 2, 1, 9, 36, 2, 10, 2, 0, 2, 5, 0,...
## $ `3/31/20` <dbl> 0, 7, 19, 0, 3, 5, 2, 1, 9, 36, 2, 11, 3, 0, 2, 5, 0,...
## $ `4/1/20` <dbl> 0, 10, 23, 0, 3, 5, 2, 1, 11, 45, 2, 13, 4, 2, 3, 6, ...
## $ `4/2/20` <dbl> 0, 10, 25, 0, 4, 6, 2, 1, 12, 67, 4, 14, 4, 2, 7, 6, ...
## $ `4/3/20` <dbl> 0, 12, 28, 1, 4, 9, 2, 1, 20, 81, 5, 15, 4, 3, 8, 7, ...
## $ `4/4/20` <dbl> 0, 12, 29, 2, 4, 10, 2, 1, 21, 87, 6, 15, 4, 7, 9, 7,...
## $ `4/5/20` <dbl> 0, 12, 34, 2, 7, 10, 2, 1, 24, 90, 6, 18, 5, 9, 9, 7,...
## $ `4/6/20` <dbl> 0, 12, 38, 3, 7, 10, 2, 1, 38, 96, 6, 20, 6, 9, 9, 9,...
## $ `4/7/20` <dbl> 0, 12, 42, 3, 8, 10, 2, 2, 48, 102, 6, 20, 6, 10, 9, ...
## $ `4/8/20` <dbl> 0, 12, 49, 3, 9, 10, 3, 3, 52, 140, 7, 22, 6, 10, 11,...
## $ `4/9/20` <dbl> 0, 17, 59, 7, 11, 11, 4, 3, 54, 161, 7, 25, 6, 13, 11...
## $ `4/10/20` <dbl> 0, 17, 59, 9, 11, 12, 4, 3, 54, 171, 7, 27, 7, 13, 11...
## $ `4/11/20` <dbl> 0, 19, 66, 10, 13, 12, 4, 6, 57, 184, 7, 30, 9, 15, 1...
## $ `4/12/20` <dbl> 0, 19, 71, 10, 16, 13, 4, 7, 60, 200, 9, 30, 10, 19, ...
## $ `4/13/20` <dbl> 0, 19, 78, 10, 17, 15, 6, 8, 61, 212, 9, 33, 10, 19, ...
## $ `4/14/20` <dbl> 0, 23, 87, 11, 17, 16, 8, 8, 62, 216, 9, 33, 12, 21, ...
## $ `4/15/20` <dbl> 0, 25, 98, 13, 19, 17, 8, 11, 62, 227, 10, 37, 13, 22...
## $ `4/16/20` <dbl> 0, 25, 102, 14, 23, 18, 8, 11, 63, 234, 11, 37, 13, 2...
## $ `4/17/20` <dbl> 0, 25, 103, 15, 23, 20, 8, 13, 63, 236, 12, 37, 13, 2...
## $ `4/18/20` <dbl> 0, 25, 109, 18, 26, 20, 9, 13, 66, 240, 12, 39, 14, 2...
## $ `4/19/20` <dbl> 0, 27, 114, 20, 28, 21, 9, 14, 72, 246, 12, 42, 14, 2...
## $ `4/20/20` <dbl> 0, 28, 117, 22, 32, 22, 11, 14, 80, 257, 12, 43, 17, ...
## $ `4/21/20` <dbl> 0, 30, 123, 28, 32, 26, 11, 15, 83, 259, 12, 44, 18, ...
## $ `4/22/20` <dbl> 0, 32, 132, 29, 33, 29, 11, 17, 85, 270, 12, 46, 21, ...
## $ `4/23/20` <dbl> 0, 33, 143, 30, 33, 31, 12, 19, 88, 275, 12, 47, 22, ...
## $ `4/24/20` <dbl> 0, 36, 147, 32, 34, 31, 12, 21, 89, 282, 12, 49, 25, ...
## $ `4/25/20` <dbl> 0, 37, 154, 33, 35, 31, 12, 28, 90, 284, 12, 49, 27, ...
## $ `4/26/20` <dbl> 0, 37, 161, 33, 38, 34, 12, 32, 90, 285, 14, 51, 32, ...
## $ `4/27/20` <dbl> 0, 39, 168, 35, 42, 34, 12, 34, 90, 289, 14, 51, 39, ...
## $ `4/28/20` <dbl> 0, 40, 171, 37, 42, 34, 12, 45, 92, 290, 15, 52, 39, ...
## $ `4/29/20` <dbl> 0, 42, 173, 37, 42, 36, 12, 51, 93, 290, 15, 52, 39, ...
## $ `4/30/20` <dbl> 0, 42, 174, 39, 42, 37, 13, 53, 93, 290, 15, 52, 43, ...
## $ `5/1/20` <dbl> 0, 42, 175, 42, 42, 39, 14, 65, 93, 290, 15, 52, 49, ...
## $ `5/2/20` <dbl> 0, 45, 181, 43, 42, 40, 14, 92, 98, 294, 15, 54, 49, ...
## $ `5/3/20` <dbl> 0, 48, 187, 45, 42, 40, 14, 105, 105, 300, 16, 57, 49...
## $ `5/4/20` <dbl> 0, 53, 188, 45, 42, 40, 16, 114, 105, 302, 16, 58, 51...
## $ `5/5/20` <dbl> 0, 53, 189, 47, 43, 40, 18, 120, 114, 304, 17, 60, 54...
## $ `5/6/20` <dbl> 0, 58, 196, 47, 43, 42, 18, 130, 114, 306, 18, 61, 54...
## $ `5/7/20` <dbl> 0, 61, 205, 51, 44, 44, 18, 155, 120, 308, 18, 63, 56...
## $ `5/8/20` <dbl> 0, 67, 208, 53, 44, 44, 21, 162, 123, 311, 21, 63, 59...
## $ `5/9/20` <dbl> 0, 68, 216, 58, 45, 44, 22, 178, 124, 314, 22, 64, 61...
## $ `5/10/20` <dbl> 0, 74, 222, 59, 46, 44, 23, 189, 124, 316, 22, 65, 66...
## $ `5/11/20` <dbl> 0, 84, 224, 61, 46, 45, 26, 196, 125, 319, 24, 67, 67...
## $ `5/12/20` <dbl> 0, 91, 227, 67, 46, 45, 26, 224, 126, 324, 24, 69, 69...
## $ `5/13/20` <dbl> 0, 93, 231, 69, 46, 45, 28, 230, 127, 324, 24, 73, 72...
## $ `5/14/20` <dbl> 0, 103, 243, 74, 46, 45, 28, 249, 128, 326, 25, 74, 7...
## $ `5/15/20` <dbl> 0, 103, 244, 79, 49, 45, 32, 258, 129, 326, 26, 75, 8...
## $ `5/16/20` <dbl> 0, 110, 254, 79, 50, 45, 35, 271, 130, 328, 27, 77, 8...
## $ `5/17/20` <dbl> 0, 110, 254, 81, 50, 46, 35, 272, 130, 328, 27, 77, 8...
## $ `5/18/20` <dbl> 0, 120, 260, 85, 50, 47, 40, 285, 133, 329, 28, 79, 8...
## $ `5/19/20` <dbl> 0, 127, 262, 90, 51, 47, 52, 295, 133, 329, 29, 80, 9...
## $ `5/20/20` <dbl> 0, 136, 270, 96, 52, 47, 64, 312, 136, 330, 30, 83, 1...
## $ `5/21/20` <dbl> 0, 147, 270, 100, 52, 48, 71, 321, 136, 330, 31, 84, ...
## $ `5/22/20` <dbl> 0, 149, 271, 104, 55, 49, 89, 329, 137, 330, 33, 85, ...
## $ `5/23/20` <dbl> 0, 155, 273, 105, 58, 49, 105, 335, 138, 330, 33, 86,...
## $ `5/24/20` <dbl> 0, 159, 274, 110, 59, 49, 111, 344, 141, 336, 33, 87,...
## $ `5/25/20` <dbl> 0, 173, 276, 116, 62, 49, 141, 368, 147, 337, 33, 87,...
## $ `5/26/20` <dbl> 0, 189, 277, 122, 66, 51, 167, 380, 150, 338, 33, 90,...
## $ `5/27/20` <dbl> 0, 192, 281, 130, 71, 53, 176, 391, 152, 340, 33, 93,...
## $ `5/28/20` <dbl> 0, 205, 281, 132, 71, 58, 185, 392, 152, 349, 34, 97,...
## $ `5/29/20` <dbl> 0, 212, 282, 147, 71, 60, 201, 396, 153, 352, 36, 99,...
## $ `5/30/20` <dbl> 0, 216, 283, 150, 72, 61, 203, 402, 154, 353, 37, 100...
## $ `5/31/20` <dbl> 0, 220, 288, 164, 75, 62, 209, 410, 157, 355, 37, 100...
## $ `6/1/20` <dbl> 0, 233, 292, 172, 76, 63, 209, 414, 164, 358, 38, 103...
## $ `6/2/20` <dbl> 0, 238, 292, 175, 76, 63, 212, 416, 165, 358, 38, 104...
## $ `6/3/20` <dbl> 0, 239, 292, 177, 76, 63, 215, 419, 165, 359, 38, 105...
## $ `6/4/20` <dbl> 0, 241, 293, 177, 76, 63, 217, 421, 167, 360, 38, 107...
## $ `6/5/20` <dbl> 0, 248, 296, 183, 76, 64, 219, 431, 169, 363, 38, 108...
## $ `6/6/20` <dbl> 0, 259, 304, 190, 77, 70, 225, 442, 174, 373, 40, 108...
## $ `6/7/20` <dbl> 0, 265, 313, 193, 77, 72, 232, 449, 176, 378, 42, 110...
## $ `6/8/20` <dbl> 0, 272, 320, 197, 79, 73, 238, 455, 178, 383, 42, 111...
## $ `6/9/20` <dbl> 0, 282, 325, 199, 85, 75, 243, 464, 180, 391, 42, 117...
## $ `6/10/20` <dbl> 0, 295, 331, 208, 89, 79, 248, 471, 182, 401, 42, 118...
## $ `6/11/20` <dbl> 0, 312, 343, 214, 93, 87, 253, 484, 184, 417, 42, 121...
## $ `6/12/20` <dbl> 0, 323, 353, 221, 97, 95, 258, 499, 188, 427, 46, 122...
## $ `6/13/20` <dbl> 0, 331, 361, 226, 100, 102, 276, 517, 190, 438, 47, 1...
## $ `6/14/20` <dbl> 0, 357, 364, 234, 104, 110, 302, 536, 195, 453, 51, 1...
## $ `6/15/20` <dbl> 0, 368, 383, 238, 111, 116, 307, 544, 204, 475, 53, 1...
## $ `6/16/20` <dbl> 0, 373, 389, 245, 116, 121, 310, 551, 206, 485, 53, 1...
## $ `6/17/20` <dbl> 0, 375, 392, 251, 118, 123, 313, 554, 208, 486, 53, 1...
## $ `6/18/20` <dbl> 0, 400, 401, 263, 121, 130, 320, 566, 210, 501, 55, 1...
## $ `6/19/20` <dbl> 0, 411, 413, 266, 126, 139, 320, 569, 210, 507, 58, 1...
## $ `6/20/20` <dbl> 0, 431, 420, 272, 126, 143, 327, 572, 211, 516, 58, 1...
## $ `6/21/20` <dbl> 0, 434, 430, 272, 127, 149, 327, 576, 213, 521, 58, 1...
## $ `6/22/20` <dbl> 0, 442, 437, 277, 129, 153, 328, 578, 215, 528, 58, 1...
## $ `6/23/20` <dbl> 0, 453, 450, 280, 135, 159, 329, 581, 216, 534, 58, 1...
## $ `6/24/20` <dbl> 0, 469, 464, 288, 141, 168, 336, 584, 220, 543, 58, 1...
## $ `6/25/20` <dbl> 0, 479, 477, 305, 149, 176, 351, 588, 233, 549, 64, 1...
## $ `6/26/20` <dbl> 0, 488, 515, 312, 153, 184, 351, 594, 236, 559, 68, 1...
## $ `6/27/20` <dbl> 0, 498, 555, 317, 161, 188, 358, 600, 245, 561, 69, 2...
## $ `6/28/20` <dbl> 0, 503, 575, 317, 162, 189, 358, 602, 245, 561, 70, 2...
## $ `6/29/20` <dbl> 0, 527, 643, 322, 165, 199, 365, 605, 269, 585, 73, 2...
## $ `6/30/20` <dbl> 0, 537, 680, 325, 170, 208, 365, 607, 276, 590, 74, 2...
## $ `7/1/20` <dbl> 0, 553, 703, 326, 174, 218, 367, 607, 278, 595, 77, 2...
## $ `7/2/20` <dbl> 0, 561, 751, 335, 179, 222, 369, 610, 288, 611, 82, 2...
## $ `7/3/20` <dbl> 0, 568, 845, 348, 189, 230, 372, 625, 330, 625, 88, 2...
## $ `7/4/20` <dbl> 0, 591, 863, 350, 190, 234, 373, 626, 340, 637, 88, 2...
## $ `7/5/20` <dbl> 0, 615, 881, 352, 193, 239, 373, 634, 362, 642, 100, ...
## $ `7/6/20` <dbl> 0, 618, 911, 356, 197, 247, 373, 634, 384, 655, 105, ...
## $ `7/7/20` <dbl> 0, 644, 997, 360, 199, 255, 373, 634, 395, 656, 106, ...
## $ `7/8/20` <dbl> 0, 651, 1056, 366, 201, 262, 374, 639, 411, 660, 114,...
## $ `7/9/20` <dbl> 0, 661, 1131, 371, 211, 282, 375, 646, 445, 672, 115,...
## $ `7/10/20` <dbl> 0, 670, 1187, 381, 218, 292, 381, 648, 465, 679, 118,...
## $ `7/11/20` <dbl> 0, 684, 1224, 398, 224, 307, 382, 654, 500, 690, 128,...
## $ `7/12/20` <dbl> 0, 706, 1294, 403, 228, 331, 383, 655, 526, 693, 129,...
## $ `7/13/20` <dbl> 0, 728, 1359, 413, 231, 350, 383, 660, 566, 702, 136,...
## $ `7/14/20` <dbl> 0, 746, 1414, 428, 236, 366, 385, 661, 589, 712, 140,...
## $ `7/15/20` <dbl> 0, 756, 1518, 441, 242, 389, 386, 664, 655, 718, 145,...
## $ `7/16/20` <dbl> 0, 780, 1599, 459, 247, 424, 389, 669, 675, 731, 152,...
## $ `7/17/20` <dbl> 0, 789, 1689, 463, 255, 440, 393, 672, 720, 742, 157,...
## $ `7/18/20` <dbl> 0, 827, 1819, 483, 264, 458, 397, 678, 741, 756, 165,...
## $ `7/19/20` <dbl> 0, 842, 1937, 495, 269, 482, 398, 686, 785, 762, 173,...
## $ `7/20/20` <dbl> 0, 857, 2013, 503, 279, 507, 400, 689, 832, 767, 179,...
## $ `7/21/20` <dbl> 0, 865, 2102, 514, 283, 524, 401, 695, 869, 774, 182,...
## $ `7/22/20` <dbl> 0, 886, 2196, 518, 287, 547, 407, 701, 891, 782, 184,...
## $ `7/23/20` <dbl> 0, 905, 2461, 534, 289, 585, 408, 706, 934, 789, 193,...
## $ `7/24/20` <dbl> 0, 921, 2513, 539, 303, 615, 411, 711, 999, 797, 205,...
## $ `7/25/20` <dbl> 0, 932, 2662, 552, 318, 637, 414, 720, 1062, 810, 207...
## $ `7/26/20` <dbl> 0, 942, 2708, 562, 324, 646, 415, 724, 1113, 821, 209...
## $ `7/27/20` <dbl> 0, 965, 2770, 569, 334, 669, 416, 730, 1194, 825, 220...
## $ `7/28/20` <dbl> 0, 974, 2835, 575, 337, 675, 429, 734, 1243, 836, 221...
## $ `7/29/20` <dbl> 0, 974, 2835, 575, 338, 675, 429, 734, 1244, 836, 221...
## $ `7/30/20` <dbl> 0, 1002, 3028, 585, 352, 731, 435, 747, 1336, 848, 23...
## $ `7/31/20` <dbl> 0, 1015, 3101, 598, 363, 767, 437, 753, 1450, 859, 23...
## $ `8/1/20` <dbl> 0, 1030, 3142, 602, 368, 792, 443, 757, 1480, 861, 24...
## $ `8/2/20` <dbl> 0, 1052, 3223, 610, 372, 813, 445, 765, 1580, 868, 25...
## $ `8/3/20` <dbl> 0, 1066, 3265, 612, 382, 830, 446, 766, 1612, 875, 26...
## $ `8/4/20` <dbl> 0, 1073, 3320, 614, 389, 836, 449, 766, 1646, 882, 26...
## $ `8/5/20` <dbl> 0, 1073, 3380, 615, 392, 839, 452, 769, 1683, 886, 27...
## $ `8/6/20` <dbl> 0, 1096, 3438, 619, 421, 874, 458, 771, 1741, 893, 28...
## $ `8/7/20` <dbl> 0, 1113, 3504, 624, 424, 909, 462, 774, 1777, 899, 29...
## $ `8/8/20` <dbl> 0, 1134, 3564, 628, 434, 923, 471, 773, 1836, 904, 29...
## $ `8/9/20` <dbl> 0, 1215, 3606, 630, 446, 934, 472, 779, 1860, 906, 30...
## $ `8/10/20` <dbl> 0, 1215, 3714, 631, 450, 947, 474, 782, 1883, 909, 30...
## $ `8/11/20` <dbl> 0, 1215, 3736, 643, 455, 958, 489, 785, 1914, 916, 30...
## $ `8/12/20` <dbl> 0, 1241, 3776, 646, 464, 967, 500, 788, 1935, 918, 31...
## $ `8/13/20` <dbl> 0, 1250, 3813, 651, 469, 977, 501, 790, 1959, 919, 32...
## $ `8/14/20` <dbl> 0, 1252, 3860, 656, 477, 989, 502, 796, 1975, 922, 32...
## $ `8/15/20` <dbl> 0, 1262, 3909, 663, 483, 996, 503, 807, 2019, 925, 33...
## $ `8/16/20` <dbl> 0, 1273, 3948, 671, 483, 1005, 504, 811, 2037, 927, 3...
## $ `8/17/20` <dbl> 0, 1274, 3960, 672, 488, 1008, 504, 814, 2055, 928, 3...
## $ `8/18/20` <dbl> 0, 1291, 3977, 674, 490, 1034, 512, 814, 2107, 937, 3...
## $ `8/19/20` <dbl> 0, 1293, 4002, 683, 503, 1049, 530, 814, 2159, 941, 3...
## $ `8/20/20` <dbl> 0, 1293, 4035, 690, 507, 1077, 534, 814, 2214, 949, 3...
## $ `8/21/20` <dbl> 0, 1293, 4054, 690, 509, 1083, 534, 814, 2228, 952, 3...
## $ `8/22/20` <dbl> 0, 1322, 4115, 699, 516, 1096, 536, 822, 2276, 957, 3...
## $ `8/23/20` <dbl> 0, 1324, 4147, 702, 523, 1099, 536, 824, 2286, 958, 3...
## $ `8/24/20` <dbl> 0, 1351, 4167, 720, 526, 1135, 536, 825, 2327, 971, 3...
## $ `8/25/20` <dbl> 0, 1355, 4190, 724, 527, 1160, 536, 826, 2345, 973, 3...
## $ `8/26/20` <dbl> 0, 1366, 4265, 732, 530, 1195, 537, 833, 2400, 983, 3...
## $ `8/27/20` <dbl> 0, 1377, 4311, 739, 533, 1213, 538, 839, 2413, 1011, ...
## $ `8/28/20` <dbl> 0, 1389, 4347, 745, 535, 1219, 541, 840, 2443, 1017, ...
## $ `8/29/20` <dbl> 0, 1400, 4424, 753, 540, 1248, 546, 855, 2499, 1024, ...
## $ `8/30/20` <dbl> 0, 1438, 4525, 757, 550, 1277, 550, 864, 2533, 1027, ...
## $ `8/31/20` <dbl> 0, 1442, 4545, 757, 554, 1287, 551, 866, 2567, 1033, ...
## $ `9/1/20` <dbl> 0, 1453, 4568, 764, 558, 1303, 559, 871, 2619, 1041, ...
## Observations: 715,680
## Variables: 6
## $ countyFIPS <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ countyName <chr> "Statewide Unallocated", "Statewide Unallocated", "State...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "A...
## $ stateFIPS <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ date <date> 2020-01-22, 2020-01-23, 2020-01-24, 2020-01-25, 2020-01...
## $ cumCases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...## Parsed with column specification:
## cols(
## .default = col_double(),
## `County Name` = col_character(),
## State = col_character()
## )
## See spec(...) for full column specifications.## Observations: 3,195
## Variables: 228
## $ countyFIPS <dbl> 0, 1001, 1003, 1005, 1007, 1009, 1011, 1013, 1015, 10...
## $ `County Name` <chr> "Statewide Unallocated", "Autauga County", "Baldwin C...
## $ State <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL",...
## $ stateFIPS <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ `1/22/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/23/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/24/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/25/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/26/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/27/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/28/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/29/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/30/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/31/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/1/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/2/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/3/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/4/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/5/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/6/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/7/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/8/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/9/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/10/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/11/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/12/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/13/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/14/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/15/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/16/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/17/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/18/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/19/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/20/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/21/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/22/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/23/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/24/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/25/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/26/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/27/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/28/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/29/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/1/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/2/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/3/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/4/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/5/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/6/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/7/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/8/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/9/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/10/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/11/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/12/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/13/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/14/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/15/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/16/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/17/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/18/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/19/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/20/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/21/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/22/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/23/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/24/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/25/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/26/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/27/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/28/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/29/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/30/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/31/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `4/1/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `4/2/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `4/3/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `4/4/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `4/5/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `4/6/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `4/7/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `4/8/20` <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `4/9/20` <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `4/10/20` <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 1,...
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## $ `4/15/20` <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 1,...
## $ `4/16/20` <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 9, 0, 0, 0, 0, 0, 0, 0, 1,...
## $ `4/17/20` <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 1, 11, 0, 0, 0, 0, 0, 0, 0, 1...
## $ `4/18/20` <dbl> 0, 1, 1, 0, 0, 0, 0, 0, 2, 11, 0, 0, 0, 0, 0, 0, 0, 1...
## $ `4/19/20` <dbl> 0, 1, 1, 0, 0, 0, 0, 0, 2, 11, 0, 0, 0, 0, 0, 0, 0, 1...
## $ `4/20/20` <dbl> 0, 1, 1, 0, 0, 0, 0, 0, 2, 11, 0, 0, 0, 0, 0, 0, 0, 1...
## $ `4/21/20` <dbl> 0, 1, 2, 0, 0, 0, 0, 0, 3, 13, 0, 0, 0, 1, 0, 0, 0, 1...
## $ `4/22/20` <dbl> 0, 1, 2, 0, 0, 0, 0, 0, 3, 16, 0, 0, 0, 1, 0, 1, 0, 1...
## $ `4/23/20` <dbl> 0, 2, 2, 0, 0, 0, 0, 0, 3, 16, 0, 1, 0, 1, 1, 1, 0, 1...
## $ `4/24/20` <dbl> 0, 2, 2, 0, 0, 0, 0, 0, 3, 17, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `4/25/20` <dbl> 0, 2, 2, 0, 0, 0, 0, 0, 3, 18, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `4/26/20` <dbl> 0, 2, 2, 0, 0, 0, 0, 1, 3, 18, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `4/27/20` <dbl> 0, 3, 2, 0, 0, 0, 0, 1, 3, 18, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `4/28/20` <dbl> 0, 3, 2, 0, 0, 0, 0, 1, 3, 19, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `4/29/20` <dbl> 0, 3, 2, 1, 0, 0, 0, 1, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `4/30/20` <dbl> 0, 3, 3, 1, 0, 0, 0, 1, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/1/20` <dbl> 0, 3, 4, 1, 0, 0, 0, 1, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
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## $ `5/3/20` <dbl> 0, 3, 4, 1, 0, 0, 0, 1, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/4/20` <dbl> 0, 3, 4, 1, 0, 0, 0, 1, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/5/20` <dbl> 0, 3, 5, 1, 0, 0, 0, 2, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/6/20` <dbl> 0, 3, 5, 1, 0, 0, 1, 2, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/7/20` <dbl> 0, 3, 5, 1, 0, 0, 1, 2, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/8/20` <dbl> 0, 3, 5, 1, 1, 0, 1, 3, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/9/20` <dbl> 0, 3, 5, 1, 1, 0, 1, 6, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/10/20` <dbl> 0, 3, 5, 1, 1, 0, 1, 6, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/11/20` <dbl> 0, 3, 6, 1, 1, 0, 1, 6, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/12/20` <dbl> 0, 3, 7, 1, 1, 0, 1, 6, 3, 21, 0, 1, 1, 1, 1, 1, 1, 2...
## $ `5/13/20` <dbl> 0, 3, 7, 1, 1, 0, 1, 6, 3, 22, 0, 1, 2, 1, 1, 1, 1, 2...
## $ `5/14/20` <dbl> 0, 3, 8, 1, 1, 0, 1, 8, 3, 22, 0, 1, 3, 2, 2, 1, 1, 2...
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## $ `5/17/20` <dbl> 0, 3, 8, 1, 1, 1, 1, 9, 3, 22, 0, 1, 3, 2, 2, 1, 1, 2...
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## $ `5/19/20` <dbl> 0, 3, 8, 1, 1, 1, 1, 10, 3, 22, 0, 1, 3, 2, 2, 1, 1, ...
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## $ `5/21/20` <dbl> 0, 3, 8, 1, 1, 1, 1, 11, 3, 23, 0, 1, 3, 2, 2, 1, 1, ...
## $ `5/22/20` <dbl> 0, 3, 9, 1, 1, 1, 1, 11, 3, 23, 2, 1, 4, 2, 2, 1, 1, ...
## $ `5/23/20` <dbl> 0, 3, 9, 1, 1, 1, 1, 11, 3, 23, 2, 1, 4, 2, 2, 1, 1, ...
## $ `5/24/20` <dbl> 0, 3, 9, 1, 1, 1, 1, 12, 3, 23, 2, 1, 4, 2, 2, 1, 1, ...
## $ `5/25/20` <dbl> 0, 3, 9, 1, 1, 1, 3, 12, 3, 24, 2, 1, 4, 2, 2, 1, 1, ...
## $ `5/26/20` <dbl> 0, 3, 9, 1, 1, 1, 3, 13, 3, 24, 2, 1, 7, 2, 2, 1, 1, ...
## $ `5/27/20` <dbl> 0, 3, 9, 1, 1, 1, 3, 13, 3, 24, 2, 1, 7, 2, 2, 1, 1, ...
## $ `5/28/20` <dbl> 0, 3, 9, 1, 1, 1, 4, 15, 3, 24, 2, 1, 8, 2, 2, 1, 1, ...
## $ `5/29/20` <dbl> 0, 3, 9, 1, 1, 1, 4, 16, 3, 24, 3, 1, 8, 2, 2, 1, 1, ...
## $ `5/30/20` <dbl> 0, 4, 9, 1, 1, 1, 4, 17, 3, 25, 3, 1, 8, 2, 2, 1, 1, ...
## $ `5/31/20` <dbl> 0, 4, 9, 1, 1, 1, 5, 18, 3, 25, 3, 1, 8, 2, 2, 1, 1, ...
## $ `6/1/20` <dbl> 0, 5, 9, 1, 1, 1, 6, 18, 3, 25, 3, 1, 10, 2, 2, 1, 1,...
## $ `6/2/20` <dbl> 0, 5, 9, 1, 1, 1, 6, 18, 3, 26, 3, 1, 10, 2, 2, 1, 1,...
## $ `6/3/20` <dbl> 0, 5, 9, 1, 1, 1, 6, 18, 3, 26, 3, 1, 10, 2, 2, 1, 1,...
## $ `6/4/20` <dbl> 0, 5, 9, 1, 1, 1, 6, 18, 3, 26, 3, 1, 10, 2, 2, 1, 1,...
## $ `6/5/20` <dbl> 0, 5, 9, 1, 1, 1, 7, 21, 3, 26, 3, 1, 10, 2, 2, 1, 1,...
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## $ `6/10/20` <dbl> 0, 6, 9, 1, 1, 1, 8, 24, 3, 26, 4, 2, 11, 3, 2, 1, 1,...
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## $ `6/12/20` <dbl> 0, 6, 9, 1, 1, 1, 8, 25, 3, 26, 5, 2, 11, 3, 2, 1, 1,...
## $ `6/13/20` <dbl> 0, 6, 9, 1, 1, 1, 8, 25, 3, 26, 5, 2, 11, 3, 2, 1, 1,...
## $ `6/14/20` <dbl> 0, 6, 9, 1, 1, 1, 8, 25, 3, 26, 5, 2, 11, 3, 2, 1, 1,...
## $ `6/15/20` <dbl> 0, 6, 9, 1, 1, 1, 9, 25, 3, 26, 5, 2, 11, 3, 2, 1, 1,...
## $ `6/16/20` <dbl> 0, 7, 9, 1, 1, 1, 9, 25, 4, 26, 5, 2, 11, 3, 2, 1, 1,...
## $ `6/17/20` <dbl> 0, 7, 9, 1, 1, 1, 9, 25, 4, 26, 5, 2, 11, 3, 2, 1, 1,...
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## $ `6/19/20` <dbl> 0, 8, 9, 1, 1, 1, 10, 26, 4, 27, 6, 3, 11, 4, 2, 1, 1...
## $ `6/20/20` <dbl> 0, 9, 9, 1, 1, 1, 10, 26, 4, 27, 6, 3, 12, 4, 2, 1, 1...
## $ `6/21/20` <dbl> 0, 9, 9, 1, 1, 1, 10, 26, 4, 27, 6, 3, 12, 4, 2, 1, 1...
## $ `6/22/20` <dbl> 0, 9, 9, 1, 1, 1, 10, 26, 4, 27, 6, 3, 12, 4, 2, 1, 1...
## $ `6/23/20` <dbl> 0, 9, 9, 1, 1, 1, 10, 27, 5, 27, 7, 3, 12, 4, 2, 1, 1...
## $ `6/24/20` <dbl> 0, 11, 9, 1, 1, 1, 10, 27, 5, 27, 7, 3, 12, 5, 2, 1, ...
## $ `6/25/20` <dbl> 0, 11, 9, 1, 1, 1, 10, 27, 5, 27, 7, 3, 12, 5, 2, 1, ...
## $ `6/26/20` <dbl> 0, 11, 9, 1, 1, 1, 10, 27, 5, 27, 7, 3, 12, 5, 2, 1, ...
## $ `6/27/20` <dbl> 0, 12, 10, 1, 1, 1, 10, 27, 5, 27, 7, 3, 12, 5, 2, 1,...
## $ `6/28/20` <dbl> 0, 12, 10, 1, 1, 1, 10, 27, 5, 27, 7, 3, 12, 5, 2, 1,...
## $ `6/29/20` <dbl> 0, 12, 10, 1, 1, 1, 10, 27, 5, 27, 7, 3, 12, 5, 2, 1,...
## $ `6/30/20` <dbl> 0, 12, 10, 1, 1, 1, 10, 27, 5, 27, 7, 3, 12, 5, 2, 1,...
## $ `7/1/20` <dbl> 0, 12, 10, 1, 1, 1, 10, 27, 5, 27, 7, 3, 12, 5, 2, 1,...
## $ `7/2/20` <dbl> 0, 13, 10, 1, 1, 1, 10, 27, 5, 27, 7, 3, 12, 6, 2, 1,...
## $ `7/3/20` <dbl> 0, 13, 10, 2, 1, 1, 10, 28, 5, 27, 7, 3, 12, 6, 2, 1,...
## $ `7/4/20` <dbl> 0, 13, 10, 2, 1, 1, 11, 28, 5, 27, 7, 3, 12, 6, 2, 1,...
## $ `7/5/20` <dbl> 0, 13, 10, 2, 1, 1, 11, 28, 5, 27, 7, 3, 12, 6, 2, 1,...
## $ `7/6/20` <dbl> 0, 13, 10, 2, 1, 1, 11, 28, 5, 27, 7, 3, 12, 6, 2, 1,...
## $ `7/7/20` <dbl> 0, 13, 10, 2, 1, 1, 11, 28, 5, 27, 7, 3, 12, 6, 2, 1,...
## $ `7/8/20` <dbl> 0, 13, 10, 2, 1, 1, 11, 28, 5, 27, 7, 3, 12, 6, 2, 1,...
## $ `7/9/20` <dbl> 0, 14, 11, 2, 1, 1, 11, 28, 5, 27, 7, 3, 12, 6, 2, 1,...
## $ `7/10/20` <dbl> 0, 15, 12, 2, 1, 1, 11, 29, 5, 27, 7, 3, 12, 6, 2, 1,...
## $ `7/11/20` <dbl> 0, 15, 12, 2, 1, 1, 11, 29, 5, 27, 7, 3, 12, 6, 2, 1,...
## $ `7/12/20` <dbl> 0, 16, 12, 2, 1, 1, 11, 29, 5, 30, 7, 3, 12, 6, 2, 1,...
## $ `7/13/20` <dbl> 0, 16, 12, 2, 1, 1, 11, 29, 5, 30, 7, 3, 12, 6, 2, 1,...
## $ `7/14/20` <dbl> 0, 18, 12, 3, 2, 1, 11, 31, 6, 32, 7, 4, 12, 6, 2, 1,...
## $ `7/15/20` <dbl> 0, 19, 13, 3, 2, 1, 11, 31, 6, 32, 7, 4, 12, 6, 2, 1,...
## $ `7/16/20` <dbl> 0, 20, 14, 3, 2, 1, 11, 32, 6, 32, 7, 4, 12, 6, 2, 1,...
## $ `7/17/20` <dbl> 0, 21, 14, 3, 2, 1, 11, 33, 6, 32, 7, 4, 12, 6, 2, 1,...
## $ `7/18/20` <dbl> 0, 21, 15, 3, 2, 1, 11, 33, 6, 33, 7, 4, 12, 7, 2, 1,...
## $ `7/19/20` <dbl> 0, 21, 15, 3, 2, 1, 11, 33, 6, 33, 7, 4, 12, 7, 2, 1,...
## $ `7/20/20` <dbl> 0, 21, 15, 4, 2, 1, 11, 33, 6, 33, 7, 4, 12, 7, 2, 1,...
## $ `7/21/20` <dbl> 0, 21, 16, 4, 2, 1, 11, 34, 6, 33, 7, 4, 12, 7, 2, 1,...
## $ `7/22/20` <dbl> 0, 21, 16, 4, 2, 1, 12, 34, 6, 34, 7, 5, 12, 8, 2, 1,...
## $ `7/23/20` <dbl> 0, 21, 17, 4, 2, 1, 12, 35, 6, 34, 7, 5, 12, 9, 2, 1,...
## $ `7/24/20` <dbl> 0, 21, 18, 4, 2, 1, 12, 35, 6, 37, 8, 5, 12, 9, 3, 1,...
## $ `7/25/20` <dbl> 0, 21, 18, 4, 2, 1, 12, 35, 6, 37, 8, 5, 12, 9, 3, 1,...
## $ `7/26/20` <dbl> 0, 21, 18, 4, 2, 1, 12, 35, 6, 37, 8, 5, 12, 9, 3, 1,...
## $ `7/27/20` <dbl> 0, 21, 18, 4, 2, 1, 12, 36, 6, 38, 8, 6, 12, 9, 4, 1,...
## $ `7/28/20` <dbl> 0, 21, 18, 4, 2, 1, 12, 36, 6, 38, 8, 6, 12, 9, 4, 1,...
## $ `7/29/20` <dbl> 0, 21, 21, 4, 2, 3, 12, 36, 6, 38, 8, 6, 12, 9, 4, 1,...
## $ `7/30/20` <dbl> 0, 21, 21, 5, 2, 3, 12, 36, 8, 38, 8, 6, 12, 9, 5, 1,...
## $ `7/31/20` <dbl> 0, 21, 22, 5, 2, 3, 12, 36, 9, 38, 8, 6, 12, 9, 5, 1,...
## $ `8/1/20` <dbl> 0, 21, 22, 5, 2, 3, 12, 36, 9, 38, 8, 6, 12, 9, 5, 1,...
## $ `8/2/20` <dbl> 0, 21, 23, 5, 3, 3, 12, 36, 9, 38, 8, 7, 12, 9, 5, 1,...
## $ `8/3/20` <dbl> 0, 21, 24, 5, 3, 3, 12, 36, 9, 38, 8, 7, 12, 9, 5, 1,...
## $ `8/4/20` <dbl> 0, 21, 24, 5, 3, 3, 12, 36, 12, 38, 8, 7, 12, 9, 5, 1...
## $ `8/5/20` <dbl> 0, 22, 24, 5, 4, 3, 12, 36, 13, 38, 8, 7, 12, 9, 5, 1...
## $ `8/6/20` <dbl> 0, 22, 25, 5, 4, 3, 12, 36, 13, 38, 8, 7, 12, 9, 5, 1...
## $ `8/7/20` <dbl> 0, 22, 25, 5, 4, 3, 12, 36, 13, 38, 8, 8, 12, 9, 5, 1...
## $ `8/8/20` <dbl> 0, 22, 26, 5, 5, 4, 12, 37, 13, 38, 8, 8, 12, 9, 5, 1...
## $ `8/9/20` <dbl> 0, 22, 27, 5, 5, 4, 12, 37, 14, 38, 8, 8, 12, 9, 5, 1...
## $ `8/10/20` <dbl> 0, 22, 28, 5, 5, 4, 12, 37, 17, 38, 9, 9, 12, 10, 5, ...
## $ `8/11/20` <dbl> 0, 23, 32, 6, 6, 5, 12, 37, 19, 38, 9, 12, 12, 10, 5,...
## $ `8/12/20` <dbl> 0, 23, 32, 6, 6, 5, 12, 37, 20, 38, 9, 12, 12, 10, 5,...
## $ `8/13/20` <dbl> 0, 23, 32, 6, 6, 5, 12, 37, 20, 38, 9, 12, 12, 10, 5,...
## $ `8/14/20` <dbl> 0, 23, 32, 6, 6, 5, 12, 37, 20, 38, 9, 12, 12, 10, 5,...
## $ `8/15/20` <dbl> 0, 23, 32, 6, 6, 5, 12, 37, 20, 38, 9, 12, 12, 10, 5,...
## $ `8/16/20` <dbl> 0, 23, 32, 6, 6, 5, 12, 37, 20, 38, 9, 12, 12, 10, 5,...
## $ `8/17/20` <dbl> 0, 23, 32, 6, 6, 5, 14, 37, 23, 38, 9, 12, 12, 10, 5,...
## $ `8/18/20` <dbl> 0, 23, 33, 6, 6, 5, 14, 37, 25, 38, 9, 12, 12, 10, 5,...
## $ `8/19/20` <dbl> 0, 23, 33, 7, 6, 5, 14, 37, 25, 38, 9, 12, 12, 10, 5,...
## $ `8/20/20` <dbl> 0, 23, 34, 7, 6, 5, 14, 37, 25, 38, 10, 12, 12, 10, 5...
## $ `8/21/20` <dbl> 0, 23, 35, 7, 6, 6, 14, 37, 25, 38, 10, 13, 12, 10, 5...
## $ `8/22/20` <dbl> 0, 23, 35, 7, 6, 6, 14, 37, 25, 38, 10, 13, 12, 10, 6...
## $ `8/23/20` <dbl> 0, 23, 35, 7, 6, 6, 14, 37, 25, 38, 10, 13, 12, 10, 6...
## $ `8/24/20` <dbl> 0, 23, 35, 7, 6, 6, 14, 37, 27, 38, 10, 13, 12, 11, 6...
## $ `8/25/20` <dbl> 0, 23, 35, 7, 6, 6, 14, 37, 28, 39, 10, 13, 12, 11, 6...
## $ `8/26/20` <dbl> 0, 23, 36, 7, 6, 7, 14, 37, 28, 39, 10, 13, 12, 12, 6...
## $ `8/27/20` <dbl> 0, 23, 37, 7, 6, 7, 14, 37, 30, 39, 12, 13, 12, 13, 6...
## $ `8/28/20` <dbl> 0, 23, 39, 7, 6, 9, 14, 37, 32, 40, 12, 13, 12, 13, 6...
## $ `8/29/20` <dbl> 0, 23, 40, 7, 7, 9, 14, 37, 35, 40, 12, 13, 12, 14, 6...
## $ `8/30/20` <dbl> 0, 23, 40, 7, 7, 10, 14, 37, 35, 40, 12, 13, 12, 14, ...
## $ `8/31/20` <dbl> 0, 23, 42, 7, 8, 11, 14, 37, 36, 40, 12, 13, 12, 14, ...
## $ `9/1/20` <dbl> 0, 24, 42, 7, 8, 11, 14, 37, 38, 40, 12, 13, 12, 14, ...
## Observations: 715,680
## Variables: 6
## $ countyFIPS <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ countyName <chr> "Statewide Unallocated", "Statewide Unallocated", "State...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "A...
## $ stateFIPS <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ date <date> 2020-01-22, 2020-01-23, 2020-01-24, 2020-01-25, 2020-01...
## $ cumDeaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...# STEP 3: Explore the cases and deaths by county (can be repeated for other counties)
casesDeathsByCounty(useDate=maxDate_20200903,
inclStates=c("FL", "GA", "SC", "AL", "MS"),
caseData=rawUSAFacts_20200903$cases,
deathData=rawUSAFacts_20200903$deaths,
highCaseAmount=80000,
highDeathAmount=2000
)
# STEP 4: Create county-level clusters (k-means, 5 clusters, minimum county population 25k)
clust_20200903_new <- prepClusterCounties(burdenFile=burden_20200903_new,
maxDate=maxDate_20200903,
minPop=25000,
hierarchical=FALSE,
minShape=3,
ratioDeathvsCase = 5,
ratioTotalvsShape = 0.5,
minDeath=100,
minCase=5000,
nCenters=5,
testCenters=1:25,
iter.max=20,
nstart=10,
seed=2009081450
)## # A tibble: 1 x 6
## cpm_mean_is0 dpm_mean_is0 cpm_mean_lt100 dpm_mean_lt100 cpm_mean_lt5000
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0 0.0352 0 0.266 0.140
## # ... with 1 more variable: dpm_mean_lt5000 <dbl>
##
## Cluster means and counts
## 1 2 3 4 5
## . 501.00 473.00 53.00 199.00 365.00
## totalCases 0.69 0.32 1.23 1.14 0.52
## totalDeaths 1.36 0.36 9.67 4.76 1.86
## cases_3 0.01 0.02 0.08 0.02 0.02
## deaths_3 0.05 0.08 0.12 0.05 0.09
## cases_4 0.04 0.06 0.39 0.14 0.17
## deaths_4 0.35 0.54 2.07 0.85 1.45
## cases_5 0.05 0.08 0.17 0.14 0.17
## deaths_5 0.29 0.34 1.29 0.99 1.72
## cases_6 0.13 0.11 0.10 0.15 0.12
## deaths_6 0.36 0.52 0.62 0.70 0.73
## cases_7 0.39 0.27 0.14 0.32 0.24
## deaths_7 1.25 0.69 0.46 1.09 0.45
## cases_8 0.38 0.32 0.12 0.24 0.25
## deaths_8 2.61 0.58 0.43 1.32 0.51# STEP 5: Extract the clusters from the clustering object
clustVec_county_20200903_new <- clust_20200903_new$objCluster$objCluster$cluster
# STEP 6: Assess the quality of the new clusters
helperACC_county_20200903_new <- helperAssessCountyClusters(clustVec_county_20200903_new,
dfPop=clust_20200903_new$countyFiltered,
dfBurden=clust_20200903_new$countyFiltered,
thruLabel="Sep 3, 2020",
plotsTogether=TRUE,
orderCluster=TRUE
)
##
## Recency is defined as 2020-08-02 through current
##
## Recency is defined as 2020-08-02 through current
# STEP 7: Create a plot of cumulative burden by cluster
helperACC_county_20200903_new %>%
select(cluster, date, pop, cases, deaths) %>%
group_by(cluster, date) %>%
summarize_if(is.numeric, sum, na.rm=TRUE) %>%
arrange(date) %>%
mutate(cpmcum=cumsum(cases)*1000000/pop, dpmcum=cumsum(deaths)*1000000/pop) %>%
ungroup() %>%
select(cluster, date, cases=cpmcum, deaths=dpmcum) %>%
pivot_longer(-c(cluster, date)) %>%
ggplot(aes(x=date, y=value, color=cluster)) +
geom_line(size=1) +
geom_text(data=~filter(., date==max(date)),
aes(x=date+lubridate::days(2), label=round(value)),
size=3,
hjust=0
) +
labs(x="", title="Cumulative burden per million people by segment", y="") +
facet_wrap(~c("cases"="Cases per million", "deaths"="Deaths per million")[name], scales="free_y") +
scale_x_date(date_breaks="1 months", date_labels="%b", expand=expand_scale(c(0, 0.1)))
# STEP 8: Add back clusters not used for analysis (code 999) and associated disease data
clusterStateData_20200903_new <- helperMakeClusterStateData(helperACC_county_20200903_new,
dfBurden=clust_20200903_new$countyDailyPerCapita,
orderCluster=TRUE
)## Joining, by = "fipsCounty"## Joining, by = "fipsCounty"# STEP 9: Run an example state-level summary (can expand to other states)
stateCountySummary(states=c("MN", "ND", "SD", "WI"),
df=changeOrderLabel(clusterStateData_20200903_new, grpVars="fipsCounty"),
keyDate=maxDate_20200903,
showQuadrants=TRUE,
showCumulative=TRUE,
facetCumulativeByState = TRUE,
showAllFactorLevels = TRUE
)
# STEP 10a: Read in updated raw data
caseFile_20200917 <- "./RInputFiles/Coronavirus/covid_confirmed_usafacts_downloaded_20200917.csv"
deathFile_20200917 <- "./RInputFiles/Coronavirus/covid_deaths_usafacts_downloaded_20200917.csv"
maxDate_20200917 <- "2020-09-15"
rawUSAFacts_20200917 <- readPivotUSAFacts(popFile=popFile,
caseFile=caseFile_20200917,
deathFile=deathFile_20200917,
unassignedDate=maxDate_20200917
)## Parsed with column specification:
## cols(
## countyFIPS = col_double(),
## `County Name` = col_character(),
## State = col_character(),
## population = col_double()
## )## Parsed with column specification:
## cols(
## .default = col_double(),
## `County Name` = col_character(),
## State = col_character()
## )## See spec(...) for full column specifications.## Parsed with column specification:
## cols(
## .default = col_double(),
## `County Name` = col_character(),
## State = col_character()
## )## See spec(...) for full column specifications.## Joining, by = c("state", "date", "unassigned")
# STEP 10b: Use existing state-level segments with updated raw data
burden_20200917 <- readUSAFacts(
caseFile=caseFile_20200917,
deathFile=deathFile_20200917,
stateClusters=test_hier5_201001$useClusters
)## Parsed with column specification:
## cols(
## .default = col_double(),
## `County Name` = col_character(),
## State = col_character()
## )## See spec(...) for full column specifications.## Observations: 3,195
## Variables: 242
## $ countyFIPS <dbl> 0, 1001, 1003, 1005, 1007, 1009, 1011, 1013, 1015, 10...
## $ `County Name` <chr> "Statewide Unallocated", "Autauga County", "Baldwin C...
## $ State <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL",...
## $ stateFIPS <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ `1/22/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
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## $ `6/17/20` <dbl> 0, 375, 392, 251, 118, 123, 313, 554, 208, 486, 53, 1...
## $ `6/18/20` <dbl> 0, 400, 401, 263, 121, 130, 320, 566, 210, 501, 55, 1...
## $ `6/19/20` <dbl> 0, 411, 413, 266, 126, 139, 320, 569, 210, 507, 58, 1...
## $ `6/20/20` <dbl> 0, 431, 420, 272, 126, 143, 327, 572, 211, 516, 58, 1...
## $ `6/21/20` <dbl> 0, 434, 430, 272, 127, 149, 327, 576, 213, 521, 58, 1...
## $ `6/22/20` <dbl> 0, 442, 437, 277, 129, 153, 328, 578, 215, 528, 58, 1...
## $ `6/23/20` <dbl> 0, 453, 450, 280, 135, 159, 329, 581, 216, 534, 58, 1...
## $ `6/24/20` <dbl> 0, 469, 464, 288, 141, 168, 336, 584, 220, 543, 58, 1...
## $ `6/25/20` <dbl> 0, 479, 477, 305, 149, 176, 351, 588, 233, 549, 64, 1...
## $ `6/26/20` <dbl> 0, 488, 515, 312, 153, 184, 351, 594, 236, 559, 68, 1...
## $ `6/27/20` <dbl> 0, 498, 555, 317, 161, 188, 358, 600, 245, 561, 69, 2...
## $ `6/28/20` <dbl> 0, 503, 575, 317, 162, 189, 358, 602, 245, 561, 70, 2...
## $ `6/29/20` <dbl> 0, 527, 643, 322, 165, 199, 365, 605, 269, 585, 73, 2...
## $ `6/30/20` <dbl> 0, 537, 680, 325, 170, 208, 365, 607, 276, 590, 74, 2...
## $ `7/1/20` <dbl> 0, 553, 703, 326, 174, 218, 367, 607, 278, 595, 77, 2...
## $ `7/2/20` <dbl> 0, 561, 751, 335, 179, 222, 369, 610, 288, 611, 82, 2...
## $ `7/3/20` <dbl> 0, 568, 845, 348, 189, 230, 372, 625, 330, 625, 88, 2...
## $ `7/4/20` <dbl> 0, 591, 863, 350, 190, 234, 373, 626, 340, 637, 88, 2...
## $ `7/5/20` <dbl> 0, 615, 881, 352, 193, 239, 373, 634, 362, 642, 100, ...
## $ `7/6/20` <dbl> 0, 618, 911, 356, 197, 247, 373, 634, 384, 655, 105, ...
## $ `7/7/20` <dbl> 0, 644, 997, 360, 199, 255, 373, 634, 395, 656, 106, ...
## $ `7/8/20` <dbl> 0, 651, 1056, 366, 201, 262, 374, 639, 411, 660, 114,...
## $ `7/9/20` <dbl> 0, 661, 1131, 371, 211, 282, 375, 646, 445, 672, 115,...
## $ `7/10/20` <dbl> 0, 670, 1187, 381, 218, 292, 381, 648, 465, 679, 118,...
## $ `7/11/20` <dbl> 0, 684, 1224, 398, 224, 307, 382, 654, 500, 690, 128,...
## $ `7/12/20` <dbl> 0, 706, 1294, 403, 228, 331, 383, 655, 526, 693, 129,...
## $ `7/13/20` <dbl> 0, 728, 1359, 413, 231, 350, 383, 660, 566, 702, 136,...
## $ `7/14/20` <dbl> 0, 746, 1414, 428, 236, 366, 385, 661, 589, 712, 140,...
## $ `7/15/20` <dbl> 0, 756, 1518, 441, 242, 389, 386, 664, 655, 718, 145,...
## $ `7/16/20` <dbl> 0, 780, 1599, 459, 247, 424, 389, 669, 675, 731, 152,...
## $ `7/17/20` <dbl> 0, 789, 1689, 463, 255, 440, 393, 672, 720, 742, 157,...
## $ `7/18/20` <dbl> 0, 827, 1819, 483, 264, 458, 397, 678, 741, 756, 165,...
## $ `7/19/20` <dbl> 0, 842, 1937, 495, 269, 482, 398, 686, 785, 762, 173,...
## $ `7/20/20` <dbl> 0, 857, 2013, 503, 279, 507, 400, 689, 832, 767, 179,...
## $ `7/21/20` <dbl> 0, 865, 2102, 514, 283, 524, 401, 695, 869, 774, 182,...
## $ `7/22/20` <dbl> 0, 886, 2196, 518, 287, 547, 407, 701, 891, 782, 184,...
## $ `7/23/20` <dbl> 0, 905, 2461, 534, 289, 585, 408, 706, 934, 789, 193,...
## $ `7/24/20` <dbl> 0, 921, 2513, 539, 303, 615, 411, 711, 999, 797, 205,...
## $ `7/25/20` <dbl> 0, 932, 2662, 552, 318, 637, 414, 720, 1062, 810, 207...
## $ `7/26/20` <dbl> 0, 942, 2708, 562, 324, 646, 415, 724, 1113, 821, 209...
## $ `7/27/20` <dbl> 0, 965, 2770, 569, 334, 669, 416, 730, 1194, 825, 220...
## $ `7/28/20` <dbl> 0, 974, 2835, 575, 337, 675, 429, 734, 1243, 836, 221...
## $ `7/29/20` <dbl> 0, 974, 2835, 575, 338, 675, 429, 734, 1244, 836, 221...
## $ `7/30/20` <dbl> 0, 1002, 3028, 585, 352, 731, 435, 747, 1336, 848, 23...
## $ `7/31/20` <dbl> 0, 1015, 3101, 598, 363, 767, 437, 753, 1450, 859, 23...
## $ `8/1/20` <dbl> 0, 1030, 3142, 602, 368, 792, 443, 757, 1480, 861, 24...
## $ `8/2/20` <dbl> 0, 1052, 3223, 610, 372, 813, 445, 765, 1580, 868, 25...
## $ `8/3/20` <dbl> 0, 1066, 3265, 612, 382, 830, 446, 766, 1612, 875, 26...
## $ `8/4/20` <dbl> 0, 1073, 3320, 614, 389, 836, 449, 766, 1646, 882, 26...
## $ `8/5/20` <dbl> 0, 1073, 3380, 615, 392, 839, 452, 769, 1683, 886, 27...
## $ `8/6/20` <dbl> 0, 1096, 3438, 619, 421, 874, 458, 771, 1741, 893, 28...
## $ `8/7/20` <dbl> 0, 1113, 3504, 624, 424, 909, 462, 774, 1777, 899, 29...
## $ `8/8/20` <dbl> 0, 1134, 3564, 628, 434, 923, 471, 773, 1836, 904, 29...
## $ `8/9/20` <dbl> 0, 1215, 3606, 630, 446, 934, 472, 779, 1860, 906, 30...
## $ `8/10/20` <dbl> 0, 1215, 3714, 631, 450, 947, 474, 782, 1883, 909, 30...
## $ `8/11/20` <dbl> 0, 1215, 3736, 643, 455, 958, 489, 785, 1914, 916, 30...
## $ `8/12/20` <dbl> 0, 1241, 3776, 646, 464, 967, 500, 788, 1935, 918, 31...
## $ `8/13/20` <dbl> 0, 1250, 3813, 651, 469, 977, 501, 790, 1959, 919, 32...
## $ `8/14/20` <dbl> 0, 1252, 3860, 656, 477, 989, 502, 796, 1975, 922, 32...
## $ `8/15/20` <dbl> 0, 1262, 3909, 663, 483, 996, 503, 807, 2019, 925, 33...
## $ `8/16/20` <dbl> 0, 1273, 3948, 671, 483, 1005, 504, 811, 2037, 927, 3...
## $ `8/17/20` <dbl> 0, 1274, 3960, 672, 488, 1008, 504, 814, 2055, 928, 3...
## $ `8/18/20` <dbl> 0, 1291, 3977, 674, 490, 1034, 512, 814, 2107, 937, 3...
## $ `8/19/20` <dbl> 0, 1293, 4002, 683, 503, 1049, 530, 814, 2159, 941, 3...
## $ `8/20/20` <dbl> 0, 1293, 4035, 690, 507, 1077, 534, 814, 2214, 949, 3...
## $ `8/21/20` <dbl> 0, 1293, 4054, 690, 509, 1083, 534, 814, 2228, 952, 3...
## $ `8/22/20` <dbl> 0, 1322, 4115, 699, 516, 1096, 536, 822, 2276, 957, 3...
## $ `8/23/20` <dbl> 0, 1324, 4147, 702, 523, 1099, 536, 824, 2286, 958, 3...
## $ `8/24/20` <dbl> 0, 1351, 4167, 720, 526, 1135, 536, 825, 2327, 971, 3...
## $ `8/25/20` <dbl> 0, 1355, 4190, 724, 527, 1160, 536, 826, 2345, 973, 3...
## $ `8/26/20` <dbl> 0, 1366, 4265, 732, 530, 1195, 537, 833, 2400, 983, 3...
## $ `8/27/20` <dbl> 0, 1377, 4311, 739, 533, 1213, 538, 839, 2413, 1011, ...
## $ `8/28/20` <dbl> 0, 1389, 4347, 745, 535, 1219, 541, 840, 2443, 1017, ...
## $ `8/29/20` <dbl> 0, 1400, 4424, 753, 540, 1248, 546, 855, 2499, 1024, ...
## $ `8/30/20` <dbl> 0, 1438, 4525, 757, 550, 1277, 550, 864, 2533, 1027, ...
## $ `8/31/20` <dbl> 0, 1442, 4545, 757, 554, 1287, 551, 866, 2567, 1033, ...
## $ `9/1/20` <dbl> 0, 1452, 4568, 764, 558, 1303, 559, 871, 2619, 1041, ...
## $ `9/2/20` <dbl> 0, 1452, 4583, 768, 562, 1308, 561, 872, 2633, 1045, ...
## $ `9/3/20` <dbl> 0, 1466, 4628, 771, 564, 1336, 563, 874, 2678, 1046, ...
## $ `9/4/20` <dbl> 0, 1475, 4654, 776, 570, 1361, 563, 881, 2747, 1054, ...
## $ `9/5/20` <dbl> 0, 1492, 4686, 776, 576, 1376, 566, 886, 2830, 1059, ...
## $ `9/6/20` <dbl> 0, 1498, 4713, 777, 581, 1379, 568, 890, 2842, 1061, ...
## $ `9/7/20` <dbl> 0, 1504, 4730, 778, 583, 1384, 568, 892, 2877, 1063, ...
## $ `9/8/20` <dbl> 0, 1508, 4757, 778, 589, 1390, 568, 892, 2891, 1064, ...
## $ `9/9/20` <dbl> 0, 1522, 4787, 778, 591, 1401, 570, 895, 2907, 1068, ...
## $ `9/10/20` <dbl> 0, 1544, 4833, 785, 594, 1430, 572, 896, 2958, 1076, ...
## $ `9/11/20` <dbl> 0, 1551, 4886, 786, 602, 1441, 573, 896, 2988, 1088, ...
## $ `9/12/20` <dbl> 0, 1565, 4922, 792, 604, 1446, 574, 898, 3047, 1094, ...
## $ `9/13/20` <dbl> 0, 1576, 4959, 794, 607, 1453, 580, 899, 3093, 1094, ...
## $ `9/14/20` <dbl> 0, 1585, 4978, 801, 610, 1464, 580, 900, 3110, 1097, ...
## $ `9/15/20` <dbl> 0, 1601, 4992, 806, 611, 1475, 581, 901, 3127, 1102, ...
## Observations: 760,410
## Variables: 6
## $ countyFIPS <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ countyName <chr> "Statewide Unallocated", "Statewide Unallocated", "State...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "A...
## $ stateFIPS <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ date <date> 2020-01-22, 2020-01-23, 2020-01-24, 2020-01-25, 2020-01...
## $ cumCases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...## Parsed with column specification:
## cols(
## .default = col_double(),
## `County Name` = col_character(),
## State = col_character()
## )
## See spec(...) for full column specifications.## Observations: 3,195
## Variables: 242
## $ countyFIPS <dbl> 0, 1001, 1003, 1005, 1007, 1009, 1011, 1013, 1015, 10...
## $ `County Name` <chr> "Statewide Unallocated", "Autauga County", "Baldwin C...
## $ State <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL",...
## $ stateFIPS <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ `1/22/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/23/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/24/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/25/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/26/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/27/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/28/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/29/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/30/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/31/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/1/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/2/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/3/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/4/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/5/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/6/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/7/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/8/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/9/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/10/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/11/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/12/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/13/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/14/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/15/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/16/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/17/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/18/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/19/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/20/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/21/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/22/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/23/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/24/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/25/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/26/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/27/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/28/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/29/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/1/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/2/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/3/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/4/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/5/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/6/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/7/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/8/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/9/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/10/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/11/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/12/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/13/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/14/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/15/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/16/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/17/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/18/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/19/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/20/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/21/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/22/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/23/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/24/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/25/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/26/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/27/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/28/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/29/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/30/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/31/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `4/1/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `4/2/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `4/3/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `4/4/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `4/5/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `4/6/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `4/7/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `4/8/20` <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `4/9/20` <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `4/10/20` <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 1,...
## $ `4/11/20` <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 1,...
## $ `4/12/20` <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 1,...
## $ `4/13/20` <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 1,...
## $ `4/14/20` <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 1,...
## $ `4/15/20` <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 1,...
## $ `4/16/20` <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 9, 0, 0, 0, 0, 0, 0, 0, 1,...
## $ `4/17/20` <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 1, 11, 0, 0, 0, 0, 0, 0, 0, 1...
## $ `4/18/20` <dbl> 0, 1, 1, 0, 0, 0, 0, 0, 2, 11, 0, 0, 0, 0, 0, 0, 0, 1...
## $ `4/19/20` <dbl> 0, 1, 1, 0, 0, 0, 0, 0, 2, 11, 0, 0, 0, 0, 0, 0, 0, 1...
## $ `4/20/20` <dbl> 0, 1, 1, 0, 0, 0, 0, 0, 2, 11, 0, 0, 0, 0, 0, 0, 0, 1...
## $ `4/21/20` <dbl> 0, 1, 2, 0, 0, 0, 0, 0, 3, 13, 0, 0, 0, 1, 0, 0, 0, 1...
## $ `4/22/20` <dbl> 0, 1, 2, 0, 0, 0, 0, 0, 3, 16, 0, 0, 0, 1, 0, 1, 0, 1...
## $ `4/23/20` <dbl> 0, 2, 2, 0, 0, 0, 0, 0, 3, 16, 0, 1, 0, 1, 1, 1, 0, 1...
## $ `4/24/20` <dbl> 0, 2, 2, 0, 0, 0, 0, 0, 3, 17, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `4/25/20` <dbl> 0, 2, 2, 0, 0, 0, 0, 0, 3, 18, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `4/26/20` <dbl> 0, 2, 2, 0, 0, 0, 0, 1, 3, 18, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `4/27/20` <dbl> 0, 3, 2, 0, 0, 0, 0, 1, 3, 18, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `4/28/20` <dbl> 0, 3, 2, 0, 0, 0, 0, 1, 3, 19, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `4/29/20` <dbl> 0, 3, 2, 1, 0, 0, 0, 1, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `4/30/20` <dbl> 0, 3, 3, 1, 0, 0, 0, 1, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/1/20` <dbl> 0, 3, 4, 1, 0, 0, 0, 1, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/2/20` <dbl> 0, 3, 4, 1, 0, 0, 0, 1, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/3/20` <dbl> 0, 3, 4, 1, 0, 0, 0, 1, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/4/20` <dbl> 0, 3, 4, 1, 0, 0, 0, 1, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/5/20` <dbl> 0, 3, 5, 1, 0, 0, 0, 2, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/6/20` <dbl> 0, 3, 5, 1, 0, 0, 1, 2, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/7/20` <dbl> 0, 3, 5, 1, 0, 0, 1, 2, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/8/20` <dbl> 0, 3, 5, 1, 1, 0, 1, 3, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/9/20` <dbl> 0, 3, 5, 1, 1, 0, 1, 6, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/10/20` <dbl> 0, 3, 5, 1, 1, 0, 1, 6, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/11/20` <dbl> 0, 3, 6, 1, 1, 0, 1, 6, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/12/20` <dbl> 0, 3, 7, 1, 1, 0, 1, 6, 3, 21, 0, 1, 1, 1, 1, 1, 1, 2...
## $ `5/13/20` <dbl> 0, 3, 7, 1, 1, 0, 1, 6, 3, 22, 0, 1, 2, 1, 1, 1, 1, 2...
## $ `5/14/20` <dbl> 0, 3, 8, 1, 1, 0, 1, 8, 3, 22, 0, 1, 3, 2, 2, 1, 1, 2...
## $ `5/15/20` <dbl> 0, 3, 8, 1, 1, 0, 1, 9, 3, 22, 0, 1, 3, 2, 2, 1, 1, 2...
## $ `5/16/20` <dbl> 0, 3, 8, 1, 1, 0, 1, 9, 3, 22, 0, 1, 3, 2, 2, 1, 1, 2...
## $ `5/17/20` <dbl> 0, 3, 8, 1, 1, 1, 1, 9, 3, 22, 0, 1, 3, 2, 2, 1, 1, 2...
## $ `5/18/20` <dbl> 0, 3, 8, 1, 1, 1, 1, 10, 3, 22, 0, 1, 3, 2, 2, 1, 1, ...
## $ `5/19/20` <dbl> 0, 3, 8, 1, 1, 1, 1, 10, 3, 22, 0, 1, 3, 2, 2, 1, 1, ...
## $ `5/20/20` <dbl> 0, 3, 8, 1, 1, 1, 1, 11, 3, 23, 0, 1, 3, 2, 2, 1, 1, ...
## $ `5/21/20` <dbl> 0, 3, 8, 1, 1, 1, 1, 11, 3, 23, 0, 1, 3, 2, 2, 1, 1, ...
## $ `5/22/20` <dbl> 0, 3, 9, 1, 1, 1, 1, 11, 3, 23, 2, 1, 4, 2, 2, 1, 1, ...
## $ `5/23/20` <dbl> 0, 3, 9, 1, 1, 1, 1, 11, 3, 23, 2, 1, 4, 2, 2, 1, 1, ...
## $ `5/24/20` <dbl> 0, 3, 9, 1, 1, 1, 1, 12, 3, 23, 2, 1, 4, 2, 2, 1, 1, ...
## $ `5/25/20` <dbl> 0, 3, 9, 1, 1, 1, 3, 12, 3, 24, 2, 1, 4, 2, 2, 1, 1, ...
## $ `5/26/20` <dbl> 0, 3, 9, 1, 1, 1, 3, 13, 3, 24, 2, 1, 7, 2, 2, 1, 1, ...
## $ `5/27/20` <dbl> 0, 3, 9, 1, 1, 1, 3, 13, 3, 24, 2, 1, 7, 2, 2, 1, 1, ...
## $ `5/28/20` <dbl> 0, 3, 9, 1, 1, 1, 4, 15, 3, 24, 2, 1, 8, 2, 2, 1, 1, ...
## $ `5/29/20` <dbl> 0, 3, 9, 1, 1, 1, 4, 16, 3, 24, 3, 1, 8, 2, 2, 1, 1, ...
## $ `5/30/20` <dbl> 0, 4, 9, 1, 1, 1, 4, 17, 3, 25, 3, 1, 8, 2, 2, 1, 1, ...
## $ `5/31/20` <dbl> 0, 4, 9, 1, 1, 1, 5, 18, 3, 25, 3, 1, 8, 2, 2, 1, 1, ...
## $ `6/1/20` <dbl> 0, 5, 9, 1, 1, 1, 6, 18, 3, 25, 3, 1, 10, 2, 2, 1, 1,...
## $ `6/2/20` <dbl> 0, 5, 9, 1, 1, 1, 6, 18, 3, 26, 3, 1, 10, 2, 2, 1, 1,...
## $ `6/3/20` <dbl> 0, 5, 9, 1, 1, 1, 6, 18, 3, 26, 3, 1, 10, 2, 2, 1, 1,...
## $ `6/4/20` <dbl> 0, 5, 9, 1, 1, 1, 6, 18, 3, 26, 3, 1, 10, 2, 2, 1, 1,...
## $ `6/5/20` <dbl> 0, 5, 9, 1, 1, 1, 7, 21, 3, 26, 3, 1, 10, 2, 2, 1, 1,...
## $ `6/6/20` <dbl> 0, 5, 9, 1, 1, 1, 7, 22, 3, 26, 4, 2, 10, 2, 2, 1, 1,...
## $ `6/7/20` <dbl> 0, 5, 9, 1, 1, 1, 7, 22, 3, 26, 4, 2, 10, 2, 2, 1, 1,...
## $ `6/8/20` <dbl> 0, 5, 9, 1, 1, 1, 8, 24, 3, 26, 4, 2, 10, 3, 2, 1, 1,...
## $ `6/9/20` <dbl> 0, 5, 9, 1, 1, 1, 8, 24, 3, 26, 4, 2, 10, 3, 2, 1, 1,...
## $ `6/10/20` <dbl> 0, 6, 9, 1, 1, 1, 8, 24, 3, 26, 4, 2, 11, 3, 2, 1, 1,...
## $ `6/11/20` <dbl> 0, 6, 9, 1, 1, 1, 8, 25, 3, 26, 4, 2, 11, 3, 2, 1, 1,...
## $ `6/12/20` <dbl> 0, 6, 9, 1, 1, 1, 8, 25, 3, 26, 5, 2, 11, 3, 2, 1, 1,...
## $ `6/13/20` <dbl> 0, 6, 9, 1, 1, 1, 8, 25, 3, 26, 5, 2, 11, 3, 2, 1, 1,...
## $ `6/14/20` <dbl> 0, 6, 9, 1, 1, 1, 8, 25, 3, 26, 5, 2, 11, 3, 2, 1, 1,...
## $ `6/15/20` <dbl> 0, 6, 9, 1, 1, 1, 9, 25, 3, 26, 5, 2, 11, 3, 2, 1, 1,...
## $ `6/16/20` <dbl> 0, 7, 9, 1, 1, 1, 9, 25, 4, 26, 5, 2, 11, 3, 2, 1, 1,...
## $ `6/17/20` <dbl> 0, 7, 9, 1, 1, 1, 9, 25, 4, 26, 5, 2, 11, 3, 2, 1, 1,...
## $ `6/18/20` <dbl> 0, 8, 9, 1, 1, 1, 9, 25, 4, 26, 5, 3, 11, 4, 2, 1, 1,...
## $ `6/19/20` <dbl> 0, 8, 9, 1, 1, 1, 10, 26, 4, 27, 6, 3, 11, 4, 2, 1, 1...
## $ `6/20/20` <dbl> 0, 9, 9, 1, 1, 1, 10, 26, 4, 27, 6, 3, 12, 4, 2, 1, 1...
## $ `6/21/20` <dbl> 0, 9, 9, 1, 1, 1, 10, 26, 4, 27, 6, 3, 12, 4, 2, 1, 1...
## $ `6/22/20` <dbl> 0, 9, 9, 1, 1, 1, 10, 26, 4, 27, 6, 3, 12, 4, 2, 1, 1...
## $ `6/23/20` <dbl> 0, 9, 9, 1, 1, 1, 10, 27, 5, 27, 7, 3, 12, 4, 2, 1, 1...
## $ `6/24/20` <dbl> 0, 11, 9, 1, 1, 1, 10, 27, 5, 27, 7, 3, 12, 5, 2, 1, ...
## $ `6/25/20` <dbl> 0, 11, 9, 1, 1, 1, 10, 27, 5, 27, 7, 3, 12, 5, 2, 1, ...
## $ `6/26/20` <dbl> 0, 11, 9, 1, 1, 1, 10, 27, 5, 27, 7, 3, 12, 5, 2, 1, ...
## $ `6/27/20` <dbl> 0, 12, 10, 1, 1, 1, 10, 27, 5, 27, 7, 3, 12, 5, 2, 1,...
## $ `6/28/20` <dbl> 0, 12, 10, 1, 1, 1, 10, 27, 5, 27, 7, 3, 12, 5, 2, 1,...
## $ `6/29/20` <dbl> 0, 12, 10, 1, 1, 1, 10, 27, 5, 27, 7, 3, 12, 5, 2, 1,...
## $ `6/30/20` <dbl> 0, 12, 10, 1, 1, 1, 10, 27, 5, 27, 7, 3, 12, 5, 2, 1,...
## $ `7/1/20` <dbl> 0, 12, 10, 1, 1, 1, 10, 27, 5, 27, 7, 3, 12, 5, 2, 1,...
## $ `7/2/20` <dbl> 0, 13, 10, 1, 1, 1, 10, 27, 5, 27, 7, 3, 12, 6, 2, 1,...
## $ `7/3/20` <dbl> 0, 13, 10, 2, 1, 1, 10, 28, 5, 27, 7, 3, 12, 6, 2, 1,...
## $ `7/4/20` <dbl> 0, 13, 10, 2, 1, 1, 11, 28, 5, 27, 7, 3, 12, 6, 2, 1,...
## $ `7/5/20` <dbl> 0, 13, 10, 2, 1, 1, 11, 28, 5, 27, 7, 3, 12, 6, 2, 1,...
## $ `7/6/20` <dbl> 0, 13, 10, 2, 1, 1, 11, 28, 5, 27, 7, 3, 12, 6, 2, 1,...
## $ `7/7/20` <dbl> 0, 13, 10, 2, 1, 1, 11, 28, 5, 27, 7, 3, 12, 6, 2, 1,...
## $ `7/8/20` <dbl> 0, 13, 10, 2, 1, 1, 11, 28, 5, 27, 7, 3, 12, 6, 2, 1,...
## $ `7/9/20` <dbl> 0, 14, 11, 2, 1, 1, 11, 28, 5, 27, 7, 3, 12, 6, 2, 1,...
## $ `7/10/20` <dbl> 0, 15, 12, 2, 1, 1, 11, 29, 5, 27, 7, 3, 12, 6, 2, 1,...
## $ `7/11/20` <dbl> 0, 15, 12, 2, 1, 1, 11, 29, 5, 27, 7, 3, 12, 6, 2, 1,...
## $ `7/12/20` <dbl> 0, 16, 12, 2, 1, 1, 11, 29, 5, 30, 7, 3, 12, 6, 2, 1,...
## $ `7/13/20` <dbl> 0, 16, 12, 2, 1, 1, 11, 29, 5, 30, 7, 3, 12, 6, 2, 1,...
## $ `7/14/20` <dbl> 0, 18, 12, 3, 2, 1, 11, 31, 6, 32, 7, 4, 12, 6, 2, 1,...
## $ `7/15/20` <dbl> 0, 19, 13, 3, 2, 1, 11, 31, 6, 32, 7, 4, 12, 6, 2, 1,...
## $ `7/16/20` <dbl> 0, 20, 14, 3, 2, 1, 11, 32, 6, 32, 7, 4, 12, 6, 2, 1,...
## $ `7/17/20` <dbl> 0, 21, 14, 3, 2, 1, 11, 33, 6, 32, 7, 4, 12, 6, 2, 1,...
## $ `7/18/20` <dbl> 0, 21, 15, 3, 2, 1, 11, 33, 6, 33, 7, 4, 12, 7, 2, 1,...
## $ `7/19/20` <dbl> 0, 21, 15, 3, 2, 1, 11, 33, 6, 33, 7, 4, 12, 7, 2, 1,...
## $ `7/20/20` <dbl> 0, 21, 15, 4, 2, 1, 11, 33, 6, 33, 7, 4, 12, 7, 2, 1,...
## $ `7/21/20` <dbl> 0, 21, 16, 4, 2, 1, 11, 34, 6, 33, 7, 4, 12, 7, 2, 1,...
## $ `7/22/20` <dbl> 0, 21, 16, 4, 2, 1, 12, 34, 6, 34, 7, 5, 12, 8, 2, 1,...
## $ `7/23/20` <dbl> 0, 21, 17, 4, 2, 1, 12, 35, 6, 34, 7, 5, 12, 9, 2, 1,...
## $ `7/24/20` <dbl> 0, 21, 18, 4, 2, 1, 12, 35, 6, 37, 8, 5, 12, 9, 3, 1,...
## $ `7/25/20` <dbl> 0, 21, 18, 4, 2, 1, 12, 35, 6, 37, 8, 5, 12, 9, 3, 1,...
## $ `7/26/20` <dbl> 0, 21, 18, 4, 2, 1, 12, 35, 6, 37, 8, 5, 12, 9, 3, 1,...
## $ `7/27/20` <dbl> 0, 21, 18, 4, 2, 1, 12, 36, 6, 38, 8, 6, 12, 9, 4, 1,...
## $ `7/28/20` <dbl> 0, 21, 18, 4, 2, 1, 12, 36, 6, 38, 8, 6, 12, 9, 4, 1,...
## $ `7/29/20` <dbl> 0, 21, 21, 4, 2, 3, 12, 36, 6, 38, 8, 6, 12, 9, 4, 1,...
## $ `7/30/20` <dbl> 0, 21, 21, 5, 2, 3, 12, 36, 8, 38, 8, 6, 12, 9, 5, 1,...
## $ `7/31/20` <dbl> 0, 21, 22, 5, 2, 3, 12, 36, 9, 38, 8, 6, 12, 9, 5, 1,...
## $ `8/1/20` <dbl> 0, 21, 22, 5, 2, 3, 12, 36, 9, 38, 8, 6, 12, 9, 5, 1,...
## $ `8/2/20` <dbl> 0, 21, 23, 5, 3, 3, 12, 36, 9, 38, 8, 7, 12, 9, 5, 1,...
## $ `8/3/20` <dbl> 0, 21, 24, 5, 3, 3, 12, 36, 9, 38, 8, 7, 12, 9, 5, 1,...
## $ `8/4/20` <dbl> 0, 21, 24, 5, 3, 3, 12, 36, 12, 38, 8, 7, 12, 9, 5, 1...
## $ `8/5/20` <dbl> 0, 22, 24, 5, 4, 3, 12, 36, 13, 38, 8, 7, 12, 9, 5, 1...
## $ `8/6/20` <dbl> 0, 22, 25, 5, 4, 3, 12, 36, 13, 38, 8, 7, 12, 9, 5, 1...
## $ `8/7/20` <dbl> 0, 22, 25, 5, 4, 3, 12, 36, 13, 38, 8, 8, 12, 9, 5, 1...
## $ `8/8/20` <dbl> 0, 22, 26, 5, 5, 4, 12, 37, 13, 38, 8, 8, 12, 9, 5, 1...
## $ `8/9/20` <dbl> 0, 22, 27, 5, 5, 4, 12, 37, 14, 38, 8, 8, 12, 9, 5, 1...
## $ `8/10/20` <dbl> 0, 22, 28, 5, 5, 4, 12, 37, 17, 38, 9, 9, 12, 10, 5, ...
## $ `8/11/20` <dbl> 0, 23, 32, 6, 6, 5, 12, 37, 19, 38, 9, 12, 12, 10, 5,...
## $ `8/12/20` <dbl> 0, 23, 32, 6, 6, 5, 12, 37, 20, 38, 9, 12, 12, 10, 5,...
## $ `8/13/20` <dbl> 0, 23, 32, 6, 6, 5, 12, 37, 20, 38, 9, 12, 12, 10, 5,...
## $ `8/14/20` <dbl> 0, 23, 32, 6, 6, 5, 12, 37, 20, 38, 9, 12, 12, 10, 5,...
## $ `8/15/20` <dbl> 0, 23, 32, 6, 6, 5, 12, 37, 20, 38, 9, 12, 12, 10, 5,...
## $ `8/16/20` <dbl> 0, 23, 32, 6, 6, 5, 12, 37, 20, 38, 9, 12, 12, 10, 5,...
## $ `8/17/20` <dbl> 0, 23, 32, 6, 6, 5, 14, 37, 23, 38, 9, 12, 12, 10, 5,...
## $ `8/18/20` <dbl> 0, 23, 33, 6, 6, 5, 14, 37, 25, 38, 9, 12, 12, 10, 5,...
## $ `8/19/20` <dbl> 0, 23, 33, 7, 6, 5, 14, 37, 25, 38, 9, 12, 12, 10, 5,...
## $ `8/20/20` <dbl> 0, 23, 34, 7, 6, 5, 14, 37, 25, 38, 10, 12, 12, 10, 5...
## $ `8/21/20` <dbl> 0, 23, 35, 7, 6, 6, 14, 37, 25, 38, 10, 13, 12, 10, 5...
## $ `8/22/20` <dbl> 0, 23, 35, 7, 6, 6, 14, 37, 25, 38, 10, 13, 12, 10, 6...
## $ `8/23/20` <dbl> 0, 23, 35, 7, 6, 6, 14, 37, 25, 38, 10, 13, 12, 10, 6...
## $ `8/24/20` <dbl> 0, 23, 35, 7, 6, 6, 14, 37, 27, 38, 10, 13, 12, 11, 6...
## $ `8/25/20` <dbl> 0, 23, 35, 7, 6, 6, 14, 37, 28, 39, 10, 13, 12, 11, 6...
## $ `8/26/20` <dbl> 0, 23, 36, 7, 6, 7, 14, 37, 28, 39, 10, 13, 12, 12, 6...
## $ `8/27/20` <dbl> 0, 23, 37, 7, 6, 7, 14, 37, 30, 39, 12, 13, 12, 13, 6...
## $ `8/28/20` <dbl> 0, 23, 39, 7, 6, 9, 14, 37, 32, 40, 12, 13, 12, 13, 6...
## $ `8/29/20` <dbl> 0, 23, 40, 7, 7, 9, 14, 37, 35, 40, 12, 13, 12, 14, 6...
## $ `8/30/20` <dbl> 0, 23, 40, 7, 7, 10, 14, 37, 35, 40, 12, 13, 12, 14, ...
## $ `8/31/20` <dbl> 0, 23, 42, 7, 8, 11, 14, 37, 36, 40, 12, 13, 12, 14, ...
## $ `9/1/20` <dbl> 0, 24, 42, 7, 8, 11, 14, 37, 38, 40, 12, 13, 12, 14, ...
## $ `9/2/20` <dbl> 0, 24, 42, 7, 8, 11, 14, 37, 38, 40, 12, 14, 12, 14, ...
## $ `9/3/20` <dbl> 0, 24, 44, 7, 8, 11, 14, 37, 38, 40, 12, 14, 12, 14, ...
## $ `9/4/20` <dbl> 0, 24, 46, 7, 9, 12, 14, 38, 38, 41, 12, 16, 12, 14, ...
## $ `9/5/20` <dbl> 0, 24, 46, 7, 9, 12, 14, 38, 38, 41, 12, 16, 12, 14, ...
## $ `9/6/20` <dbl> 0, 24, 46, 7, 9, 12, 14, 38, 38, 41, 12, 16, 12, 14, ...
## $ `9/7/20` <dbl> 0, 24, 46, 7, 9, 12, 14, 38, 38, 41, 12, 16, 12, 14, ...
## $ `9/8/20` <dbl> 0, 24, 46, 7, 9, 12, 14, 38, 38, 41, 12, 16, 12, 14, ...
## $ `9/9/20` <dbl> 0, 24, 46, 7, 9, 12, 14, 38, 38, 41, 12, 16, 12, 14, ...
## $ `9/10/20` <dbl> 0, 24, 46, 7, 9, 12, 14, 38, 38, 41, 13, 16, 12, 14, ...
## $ `9/11/20` <dbl> 0, 24, 47, 7, 9, 12, 14, 38, 38, 41, 14, 17, 12, 15, ...
## $ `9/12/20` <dbl> 0, 24, 47, 7, 9, 13, 14, 38, 38, 42, 14, 18, 12, 16, ...
## $ `9/13/20` <dbl> 0, 24, 47, 7, 9, 13, 14, 38, 38, 42, 14, 18, 12, 16, ...
## $ `9/14/20` <dbl> 0, 24, 47, 7, 9, 13, 14, 38, 38, 42, 14, 18, 12, 16, ...
## $ `9/15/20` <dbl> 0, 24, 47, 7, 9, 13, 14, 38, 38, 42, 15, 24, 12, 16, ...
## Observations: 760,410
## Variables: 6
## $ countyFIPS <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ countyName <chr> "Statewide Unallocated", "Statewide Unallocated", "State...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "A...
## $ stateFIPS <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ date <date> 2020-01-22, 2020-01-23, 2020-01-24, 2020-01-25, 2020-01...
## $ cumDeaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...# STEP 11: Compare burden in old data and new data
bind_rows(burden_20200903_new, burden_20200917, .id="source") %>%
mutate(source=factor(case_when(source==1 ~ "2020-09-03", source==2 ~ "2020-09-17", TRUE ~ "Unknown"),
levels=c("2020-09-17", "2020-09-03", "Unknown")
)
) %>%
group_by(source, date) %>%
summarize(cumDeaths=sum(cumDeaths), cumCases=sum(cumCases)) %>%
pivot_longer(-c(source, date)) %>%
ggplot(aes(x=date, y=value/1000, group=source, color=source)) +
geom_line() +
facet_wrap(~c("cumCases"="Cases", "cumDeaths"="Deaths")[name], scales="free_y") +
scale_x_date(date_breaks="1 months", date_labels="%m") +
labs(y="Burden (000s)", title="US National Coronavirus Burden by Source")
# STEP 12: Show top-level findings by segment
plotBurdenData(burden_20200917, maxDate=maxDate_20200917, minPop=10000)
# STEP 13: Create county-level clusters (k-means, 5 clusters, minimum county population 25k)
clust_20200917_new <- prepClusterCounties(burdenFile=burden_20200917,
maxDate=maxDate_20200917,
minPop=25000,
hierarchical=FALSE,
minShape=3,
ratioDeathvsCase = 5,
ratioTotalvsShape = 0.5,
minDeath=100,
minCase=5000,
nCenters=5,
testCenters=1:25,
iter.max=20,
nstart=10,
seed=2009081450
)## # A tibble: 1 x 6
## cpm_mean_is0 dpm_mean_is0 cpm_mean_lt100 dpm_mean_lt100 cpm_mean_lt5000
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0 0.0283 0 0.224 0.104
## # ... with 1 more variable: dpm_mean_lt5000 <dbl>
##
## Cluster means and counts
## 1 2 3 4 5
## . 503.00 484.00 73.00 219.00 312.00
## totalCases 0.77 0.39 1.40 1.14 0.56
## totalDeaths 1.56 0.46 9.47 4.69 1.97
## cases_3 0.01 0.02 0.06 0.01 0.02
## deaths_3 0.04 0.08 0.09 0.05 0.09
## cases_4 0.04 0.06 0.29 0.12 0.17
## deaths_4 0.32 0.53 1.61 0.76 1.46
## cases_5 0.05 0.07 0.15 0.12 0.16
## deaths_5 0.26 0.35 1.05 0.96 1.72
## cases_6 0.11 0.10 0.10 0.13 0.10
## deaths_6 0.33 0.51 0.58 0.63 0.66
## cases_7 0.34 0.23 0.20 0.29 0.20
## deaths_7 1.05 0.64 0.63 0.96 0.40
## cases_8 0.32 0.26 0.16 0.23 0.21
## deaths_8 2.09 0.52 0.78 1.22 0.42
## cases_9 0.14 0.16 0.05 0.08 0.11
## deaths_9 0.87 0.55 0.26 0.43 0.22# STEP 14: Assess the previous clusters on the updated data
helper_test_20200917 <- helperAssessCountyClusters(vecCluster=clustVec_county_20200903_new,
dfPop=clust_20200917_new$countyFiltered,
dfBurden=clust_20200917_new$countyFiltered,
thruLabel="Sep 15, 2020",
plotsTogether=TRUE,
showMap=TRUE,
clusterPlotsTogether=TRUE,
orderCluster=TRUE
)
##
## Recency is defined as 2020-08-17 through current
##
## Recency is defined as 2020-08-17 through current## Warning: Removed 1 rows containing missing values (geom_point).
# STEP 15: Create normalized data
cNorm_20200917 <- helperMakeNormData(helper_test_20200917)
# STEP 16: Assess lags for early pandemic and late pandemic
lagData_early_20200917 <- helperTestLags(cNorm_20200917, minDate="2020-03-01", maxDate="2020-05-31")
##
## The best lags are:
## # A tibble: 5 x 3
## cluster corr lag
## <fct> <dbl> <int>
## 1 5 0.971 2
## 2 2 0.809 4
## 3 1 0.937 5
## 4 4 0.972 7
## 5 3 0.989 8
lagData_late_20200917 <- helperTestLags(cNorm_20200917, minDate="2020-06-01", maxDate="2020-09-15")
##
## The best lags are:
## # A tibble: 5 x 3
## cluster corr lag
## <fct> <dbl> <int>
## 1 2 0.781 6
## 2 4 0.886 16
## 3 1 0.967 19
## 4 5 0.435 23
## 5 3 0.472 26
# STEP 17: Generate a list of key counties
keyCounties_20200917 <- helper_test_20200917 %>%
mutate(state=str_pad(state, width=5, side="left", pad="0")) %>%
filter(pop >= 100000) %>%
group_by(state, cluster) %>%
summarize(dpm=sum(dpm), pop=mean(pop)) %>%
group_by(cluster) %>%
top_n(n=3, wt=dpm) %>%
ungroup() %>%
arrange(cluster, -dpm) %>%
inner_join(select(usmap::countypop, -pop_2015), by=c("state"="fips")) %>%
mutate(countyName=paste0(cluster, " - ",
stringr::str_replace(county, "County|Parish", "("),
abbr,
")"
)
) %>%
select(-abbr, -county)
# STEP 18: Keep only key counties
cNorm_keyCounties_20200917 <- helper_test_20200917 %>%
mutate(state=str_pad(state, width=5, side="left", pad="0")) %>%
inner_join(select(keyCounties_20200917, state, countyName), by=c("state"="state")) %>%
helperMakeNormData(aggBy=c("countyName", "state", "cluster"))
# STEP 19: Create early and late lags for key counties
lagData_early_keycounties_20200917 <- helperTestLags(cNorm_keyCounties_20200917,
minDate="2020-03-01",
maxDate="2020-05-31",
aggBy=c("countyName", "state", "cluster"),
maxRatio=0.25
)
##
## The best lags are:
## # A tibble: 15 x 5
## countyName state cluster corr lag
## <chr> <chr> <fct> <dbl> <int>
## 1 1 - Anderson (SC) 45007 1 0.717 0
## 2 1 - Webb (TX) 48479 1 0.642 0
## 3 2 - Douglas (NE) 31055 2 0.517 0
## 4 2 - Pulaski (AR) 05119 2 0.644 0
## 5 1 - Highlands (FL) 12055 1 0.772 1
## 6 5 - Litchfield (CT) 09005 5 0.894 4
## 7 2 - DeKalb (IL) 17037 2 0.654 5
## 8 3 - Bronx (NY) 36005 3 0.962 6
## 9 3 - Kings (NY) 36047 3 0.945 8
## 10 3 - Queens (NY) 36081 3 0.943 8
## 11 4 - Yuma (AZ) 04027 4 0.956 11
## 12 5 - Gloucester (NJ) 34015 5 0.874 12
## 13 4 - Caddo (LA) 22017 4 0.785 13
## 14 4 - Hidalgo (TX) 48215 4 0.632 17
## 15 5 - Barnstable (MA) 25001 5 0.679 21## Warning: Removed 136 rows containing missing values (geom_path).## Warning: Removed 136 rows containing missing values (geom_path).
lagData_late_keycounties_20200917 <- helperTestLags(cNorm_keyCounties_20200917,
minDate="2020-06-01",
maxDate="2020-09-15",
aggBy=c("countyName", "state", "cluster"),
maxRatio=0.25
)
##
## The best lags are:
## # A tibble: 15 x 5
## countyName state cluster corr lag
## <chr> <chr> <fct> <dbl> <int>
## 1 5 - Litchfield (CT) 09005 5 0.179 6
## 2 1 - Highlands (FL) 12055 1 0.689 13
## 3 4 - Hidalgo (TX) 48215 4 0.758 16
## 4 2 - DeKalb (IL) 17037 2 0.130 19
## 5 2 - Douglas (NE) 31055 2 0.152 19
## 6 4 - Yuma (AZ) 04027 4 0.780 19
## 7 4 - Caddo (LA) 22017 4 0.312 24
## 8 2 - Pulaski (AR) 05119 2 0.436 25
## 9 1 - Anderson (SC) 45007 1 0.838 26
## 10 3 - Bronx (NY) 36005 3 0.755 26
## 11 3 - Kings (NY) 36047 3 0.929 26
## 12 3 - Queens (NY) 36081 3 0.734 26
## 13 5 - Gloucester (NJ) 34015 5 -0.412 29
## 14 1 - Webb (TX) 48479 1 0.738 30
## 15 5 - Barnstable (MA) 25001 5 0.560 30
# STEP 20: Run for only key counties
exploreTopCounties(helper_test_20200917, minDate="2020-03-01", maxDate="2020-05-31", nVar="pop", nKey=12)##
## *** KEY COUNTIES INCLUDE: ***
## # A tibble: 12 x 5
## state cluster countyName dpm pop
## <chr> <fct> <chr> <dbl> <dbl>
## 1 06037 5 5 - Los Angeles (CA) 625. 10039107
## 2 17031 4 4 - Cook (IL) 997. 5150233
## 3 48201 1 1 - Harris (TX) 518. 4713325
## 4 04013 4 4 - Maricopa (AZ) 707. 4485414
## 5 06073 2 2 - San Diego (CA) 222. 3338330
## 6 06059 1 1 - Orange (CA) 348. 3175692
## 7 12086 4 4 - Miami-Dade (FL) 1076. 2716940
## 8 48113 1 1 - Dallas (TX) 398. 2635516
## 9 36047 3 3 - Kings (NY) 2854. 2559903
## 10 06065 1 1 - Riverside (CA) 455. 2470546
## 11 32003 1 1 - Clark (NV) 568. 2266715
## 12 36081 3 3 - Queens (NY) 3208. 2253858
##
## The best lags are:
## # A tibble: 12 x 5
## countyName state cluster corr lag
## <chr> <chr> <fct> <dbl> <int>
## 1 1 - Clark (NV) 32003 1 0.799 0
## 2 1 - Dallas (TX) 48113 1 0.816 0
## 3 1 - Harris (TX) 48201 1 0.694 0
## 4 1 - Orange (CA) 06059 1 0.837 1
## 5 2 - San Diego (CA) 06073 2 0.861 1
## 6 4 - Cook (IL) 17031 4 0.972 4
## 7 4 - Maricopa (AZ) 04013 4 0.931 4
## 8 5 - Los Angeles (CA) 06037 5 0.874 4
## 9 3 - Kings (NY) 36047 3 0.945 8
## 10 3 - Queens (NY) 36081 3 0.943 8
## 11 1 - Riverside (CA) 06065 1 0.952 12
## 12 4 - Miami-Dade (FL) 12086 4 0.910 12## Warning: Removed 23 rows containing missing values (geom_path).## Warning: Removed 23 rows containing missing values (geom_path).
## # A tibble: 1,104 x 9
## # Groups: countyName, state, cluster [12]
## countyName state cluster date cpm7 dpm7 corr lag cpmlag
## <chr> <chr> <fct> <date> <dbl> <dbl> <dbl> <int> <dbl>
## 1 1 - Clark (NV) 32003 1 2020-03-01 0 0 0.799 0 0
## 2 1 - Dallas (TX) 48113 1 2020-03-01 0 0 0.816 0 0
## 3 1 - Harris (TX) 48201 1 2020-03-01 0 0 0.694 0 0
## 4 1 - Orange (CA) 06059 1 2020-03-01 0.0900 0 0.837 1 NA
## 5 1 - Riverside (CA) 06065 1 2020-03-01 0 0 0.952 12 NA
## 6 2 - San Diego (CA) 06073 2 2020-03-01 0 0 0.861 1 NA
## 7 3 - Kings (NY) 36047 3 2020-03-01 0 0 0.945 8 NA
## 8 3 - Queens (NY) 36081 3 2020-03-01 0 0 0.943 8 NA
## 9 4 - Cook (IL) 17031 4 2020-03-01 0.0555 0 0.972 4 NA
## 10 4 - Maricopa (AZ) 04013 4 2020-03-01 0 0 0.931 4 NA
## # ... with 1,094 more rowsexploreTopCounties(helper_test_20200917, minDate="2020-06-15", maxDate="2020-09-15", nVar="pop", nKey=12)##
## *** KEY COUNTIES INCLUDE: ***
## # A tibble: 12 x 5
## state cluster countyName dpm pop
## <chr> <fct> <chr> <dbl> <dbl>
## 1 06037 5 5 - Los Angeles (CA) 625. 10039107
## 2 17031 4 4 - Cook (IL) 997. 5150233
## 3 48201 1 1 - Harris (TX) 518. 4713325
## 4 04013 4 4 - Maricopa (AZ) 707. 4485414
## 5 06073 2 2 - San Diego (CA) 222. 3338330
## 6 06059 1 1 - Orange (CA) 348. 3175692
## 7 12086 4 4 - Miami-Dade (FL) 1076. 2716940
## 8 48113 1 1 - Dallas (TX) 398. 2635516
## 9 36047 3 3 - Kings (NY) 2854. 2559903
## 10 06065 1 1 - Riverside (CA) 455. 2470546
## 11 32003 1 1 - Clark (NV) 568. 2266715
## 12 36081 3 3 - Queens (NY) 3208. 2253858
##
## The best lags are:
## # A tibble: 12 x 5
## countyName state cluster corr lag
## <chr> <chr> <fct> <dbl> <int>
## 1 2 - San Diego (CA) 06073 2 0.771 5
## 2 3 - Kings (NY) 36047 3 0.773 12
## 3 5 - Los Angeles (CA) 06037 5 0.740 12
## 4 1 - Harris (TX) 48201 1 0.661 13
## 5 1 - Riverside (CA) 06065 1 0.505 16
## 6 4 - Maricopa (AZ) 04013 4 0.934 18
## 7 4 - Miami-Dade (FL) 12086 4 0.469 19
## 8 1 - Clark (NV) 32003 1 0.814 20
## 9 1 - Orange (CA) 06059 1 0.388 21
## 10 1 - Dallas (TX) 48113 1 0.609 30
## 11 3 - Queens (NY) 36081 3 0.768 30
## 12 4 - Cook (IL) 17031 4 -0.526 30
## # A tibble: 1,080 x 9
## # Groups: countyName, state, cluster [12]
## countyName state cluster date cpm7 dpm7 corr lag cpmlag
## <chr> <chr> <fct> <date> <dbl> <dbl> <dbl> <int> <dbl>
## 1 1 - Clark (NV) 32003 1 2020-06-15 90.4 0.756 0.814 20 NA
## 2 1 - Dallas (TX) 48113 1 2020-06-15 125. 1.52 0.609 30 NA
## 3 1 - Harris (TX) 48201 1 2020-06-15 79.0 0.940 0.661 13 NA
## 4 1 - Orange (CA) 06059 1 2020-06-15 58.7 2.16 0.388 21 NA
## 5 1 - Riverside (CA) 06065 1 2020-06-15 129. 1.79 0.505 16 NA
## 6 2 - San Diego (CA) 06073 2 2020-06-15 46.8 0.984 0.771 5 NA
## 7 3 - Kings (NY) 36047 3 2020-06-15 50.1 4.41 0.773 12 NA
## 8 3 - Queens (NY) 36081 3 2020-06-15 53.0 3.74 0.768 30 NA
## 9 4 - Cook (IL) 17031 4 2020-06-15 61.1 6.16 -0.526 30 NA
## 10 4 - Maricopa (AZ) 04013 4 2020-06-15 250. 2.10 0.934 18 NA
## # ... with 1,070 more rowsThe process is further converted to functional form as follows:
- Capability to either start the process from scratch or to leverage existing clusters at the state or county level
- Add the capability to compare a newly read file to an existing file
- Add the capability to show burden by existing segment after reading in an existing file
- Add the capability to prepare the data (create countyFiltered) without running the clustering proces
- Add the cumulative burden chart to helperAssessCountyClusters
New data are downloaed from 2020-10-03, and the updated code includes:
# STEP 1a: Define the locations for the population, cases, and deaths file
popFile <- "./RInputFiles/Coronavirus/covid_county_population_usafacts.csv"
caseFile_20201003 <- "./RInputFiles/Coronavirus/covid_confirmed_usafacts_downloaded_20201003.csv"
deathFile_20201003 <- "./RInputFiles/Coronavirus/covid_deaths_usafacts_downloaded_20201003.csv"
maxDate_20201003 <- "2020-09-30"
# STEP 1b: Read and pivot data from USA Facts; extract population data file as pop_usafacts
rawUSAFacts_20201003 <- readPivotUSAFacts(popFile=popFile,
caseFile=caseFile_20201003,
deathFile=deathFile_20201003,
unassignedDate=maxDate_20201003
)## Parsed with column specification:
## cols(
## countyFIPS = col_double(),
## `County Name` = col_character(),
## State = col_character(),
## population = col_double()
## )## Parsed with column specification:
## cols(
## .default = col_double(),
## `County Name` = col_character(),
## State = col_character()
## )## See spec(...) for full column specifications.## Parsed with column specification:
## cols(
## .default = col_double(),
## `County Name` = col_character(),
## State = col_character()
## )## See spec(...) for full column specifications.## Joining, by = c("state", "date", "unassigned")
pop_usafacts <- rawUSAFacts_20201003$pop
# STEP 2: Read case and death data (redundant), combine, and add population totals
# Add previous clusters by county rather than by state (function readUSAFacts updated)
burden_20201003 <- readUSAFacts(
caseFile=caseFile_20201003,
deathFile=deathFile_20201003,
oldFile=burden_20200903_new,
showBurdenMinPop=10000,
maxDate=maxDate_20201003,
stateClusters=NULL,
countyClusters=clustVec_county_20200903_new
)## Parsed with column specification:
## cols(
## .default = col_double(),
## `County Name` = col_character(),
## State = col_character()
## )## See spec(...) for full column specifications.## Observations: 3,195
## Variables: 258
## $ countyFIPS <dbl> 0, 1001, 1003, 1005, 1007, 1009, 1011, 1013, 1015, 10...
## $ `County Name` <chr> "Statewide Unallocated", "Autauga County", "Baldwin C...
## $ State <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL",...
## $ stateFIPS <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ `1/22/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/23/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/24/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/25/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/26/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/27/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/28/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/29/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/30/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/31/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/1/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/2/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/3/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/4/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/5/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/6/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/7/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/8/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/9/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/10/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/11/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/12/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/13/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/14/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/15/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/16/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/17/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/18/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/19/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/20/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/21/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/22/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/23/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/24/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/25/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/26/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/27/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/28/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/29/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/1/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/2/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/3/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/4/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/5/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/6/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/7/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/8/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/9/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/10/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/11/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/12/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/13/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/14/20` <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/15/20` <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/16/20` <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/17/20` <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/18/20` <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/19/20` <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/20/20` <dbl> 0, 0, 2, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/21/20` <dbl> 0, 0, 2, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/22/20` <dbl> 0, 0, 3, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/23/20` <dbl> 0, 0, 3, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/24/20` <dbl> 0, 1, 4, 0, 0, 0, 0, 0, 2, 5, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/25/20` <dbl> 0, 4, 4, 0, 0, 1, 0, 1, 2, 10, 1, 1, 0, 0, 1, 1, 0, 1...
## $ `3/26/20` <dbl> 0, 6, 5, 0, 0, 2, 2, 1, 2, 13, 1, 4, 1, 0, 1, 1, 0, 1...
## $ `3/27/20` <dbl> 0, 6, 5, 0, 0, 5, 2, 1, 3, 15, 1, 7, 1, 0, 1, 3, 0, 1...
## $ `3/28/20` <dbl> 0, 6, 10, 0, 0, 5, 2, 1, 3, 17, 1, 7, 1, 0, 2, 4, 0, ...
## $ `3/29/20` <dbl> 0, 6, 15, 0, 0, 5, 2, 1, 3, 27, 2, 8, 1, 0, 2, 5, 0, ...
## $ `3/30/20` <dbl> 0, 7, 18, 0, 2, 5, 2, 1, 9, 36, 2, 10, 2, 0, 2, 5, 0,...
## $ `3/31/20` <dbl> 0, 7, 19, 0, 3, 5, 2, 1, 9, 36, 2, 11, 3, 0, 2, 5, 0,...
## $ `4/1/20` <dbl> 0, 10, 23, 0, 3, 5, 2, 1, 11, 45, 2, 13, 4, 2, 3, 6, ...
## $ `4/2/20` <dbl> 0, 10, 25, 0, 4, 6, 2, 1, 12, 67, 4, 14, 4, 2, 7, 6, ...
## $ `4/3/20` <dbl> 0, 12, 28, 1, 4, 9, 2, 1, 20, 81, 5, 15, 4, 3, 8, 7, ...
## $ `4/4/20` <dbl> 0, 12, 29, 2, 4, 10, 2, 1, 21, 87, 6, 15, 4, 7, 9, 7,...
## $ `4/5/20` <dbl> 0, 12, 34, 2, 7, 10, 2, 1, 24, 90, 6, 18, 5, 9, 9, 7,...
## $ `4/6/20` <dbl> 0, 12, 38, 3, 7, 10, 2, 1, 38, 96, 6, 20, 6, 9, 9, 9,...
## $ `4/7/20` <dbl> 0, 12, 42, 3, 8, 10, 2, 2, 48, 102, 6, 20, 6, 10, 9, ...
## $ `4/8/20` <dbl> 0, 12, 49, 3, 9, 10, 3, 3, 52, 140, 7, 22, 6, 10, 11,...
## $ `4/9/20` <dbl> 0, 17, 59, 7, 11, 11, 4, 3, 54, 161, 7, 25, 6, 13, 11...
## $ `4/10/20` <dbl> 0, 17, 59, 9, 11, 12, 4, 3, 54, 171, 7, 27, 7, 13, 11...
## $ `4/11/20` <dbl> 0, 19, 66, 10, 13, 12, 4, 6, 57, 184, 7, 30, 9, 15, 1...
## $ `4/12/20` <dbl> 0, 19, 71, 10, 16, 13, 4, 7, 60, 200, 9, 30, 10, 19, ...
## $ `4/13/20` <dbl> 0, 19, 78, 10, 17, 15, 6, 8, 61, 212, 9, 33, 10, 19, ...
## $ `4/14/20` <dbl> 0, 23, 87, 11, 17, 16, 8, 8, 62, 216, 9, 33, 12, 21, ...
## $ `4/15/20` <dbl> 0, 25, 98, 13, 19, 17, 8, 11, 62, 227, 10, 37, 13, 22...
## $ `4/16/20` <dbl> 0, 25, 102, 14, 23, 18, 8, 11, 63, 234, 11, 37, 13, 2...
## $ `4/17/20` <dbl> 0, 25, 103, 15, 23, 20, 8, 13, 63, 236, 12, 37, 13, 2...
## $ `4/18/20` <dbl> 0, 25, 109, 18, 26, 20, 9, 13, 66, 240, 12, 39, 14, 2...
## $ `4/19/20` <dbl> 0, 27, 114, 20, 28, 21, 9, 14, 72, 246, 12, 42, 14, 2...
## $ `4/20/20` <dbl> 0, 28, 117, 22, 32, 22, 11, 14, 80, 257, 12, 43, 17, ...
## $ `4/21/20` <dbl> 0, 30, 123, 28, 32, 26, 11, 15, 83, 259, 12, 44, 18, ...
## $ `4/22/20` <dbl> 0, 32, 132, 29, 33, 29, 11, 17, 85, 270, 12, 46, 21, ...
## $ `4/23/20` <dbl> 0, 33, 143, 30, 33, 31, 12, 19, 88, 275, 12, 47, 22, ...
## $ `4/24/20` <dbl> 0, 36, 147, 32, 34, 31, 12, 21, 89, 282, 12, 49, 25, ...
## $ `4/25/20` <dbl> 0, 37, 154, 33, 35, 31, 12, 28, 90, 284, 12, 49, 27, ...
## $ `4/26/20` <dbl> 0, 37, 161, 33, 38, 34, 12, 32, 90, 285, 14, 51, 32, ...
## $ `4/27/20` <dbl> 0, 39, 168, 35, 42, 34, 12, 34, 90, 289, 14, 51, 39, ...
## $ `4/28/20` <dbl> 0, 40, 171, 37, 42, 34, 12, 45, 92, 290, 15, 52, 39, ...
## $ `4/29/20` <dbl> 0, 42, 173, 37, 42, 36, 12, 51, 93, 290, 15, 52, 39, ...
## $ `4/30/20` <dbl> 0, 42, 174, 39, 42, 37, 13, 53, 93, 290, 15, 52, 43, ...
## $ `5/1/20` <dbl> 0, 42, 175, 42, 42, 39, 14, 65, 93, 290, 15, 52, 49, ...
## $ `5/2/20` <dbl> 0, 45, 181, 43, 42, 40, 14, 92, 98, 294, 15, 54, 49, ...
## $ `5/3/20` <dbl> 0, 48, 187, 45, 42, 40, 14, 105, 105, 300, 16, 57, 49...
## $ `5/4/20` <dbl> 0, 53, 188, 45, 42, 40, 16, 114, 105, 302, 16, 58, 51...
## $ `5/5/20` <dbl> 0, 53, 189, 47, 43, 40, 18, 120, 114, 304, 17, 60, 54...
## $ `5/6/20` <dbl> 0, 58, 196, 47, 43, 42, 18, 130, 114, 306, 18, 61, 54...
## $ `5/7/20` <dbl> 0, 61, 205, 51, 44, 44, 18, 155, 120, 308, 18, 63, 56...
## $ `5/8/20` <dbl> 0, 67, 208, 53, 44, 44, 21, 162, 123, 311, 21, 63, 59...
## $ `5/9/20` <dbl> 0, 68, 216, 58, 45, 44, 22, 178, 124, 314, 22, 64, 61...
## $ `5/10/20` <dbl> 0, 74, 222, 59, 46, 44, 23, 189, 124, 316, 22, 65, 66...
## $ `5/11/20` <dbl> 0, 84, 224, 61, 46, 45, 26, 196, 125, 319, 24, 67, 67...
## $ `5/12/20` <dbl> 0, 91, 227, 67, 46, 45, 26, 224, 126, 324, 24, 69, 69...
## $ `5/13/20` <dbl> 0, 93, 231, 69, 46, 45, 28, 230, 127, 324, 24, 73, 72...
## $ `5/14/20` <dbl> 0, 103, 243, 74, 46, 45, 28, 249, 128, 326, 25, 74, 7...
## $ `5/15/20` <dbl> 0, 103, 244, 79, 49, 45, 32, 258, 129, 326, 26, 75, 8...
## $ `5/16/20` <dbl> 0, 110, 254, 79, 50, 45, 35, 271, 130, 328, 27, 77, 8...
## $ `5/17/20` <dbl> 0, 110, 254, 81, 50, 46, 35, 272, 130, 328, 27, 77, 8...
## $ `5/18/20` <dbl> 0, 120, 260, 85, 50, 47, 40, 285, 133, 329, 28, 79, 8...
## $ `5/19/20` <dbl> 0, 127, 262, 90, 51, 47, 52, 295, 133, 329, 29, 80, 9...
## $ `5/20/20` <dbl> 0, 136, 270, 96, 52, 47, 64, 312, 136, 330, 30, 83, 1...
## $ `5/21/20` <dbl> 0, 147, 270, 100, 52, 48, 71, 321, 136, 330, 31, 84, ...
## $ `5/22/20` <dbl> 0, 149, 271, 104, 55, 49, 89, 329, 137, 330, 33, 85, ...
## $ `5/23/20` <dbl> 0, 155, 273, 105, 58, 49, 105, 335, 138, 330, 33, 86,...
## $ `5/24/20` <dbl> 0, 159, 274, 110, 59, 49, 111, 344, 141, 336, 33, 87,...
## $ `5/25/20` <dbl> 0, 173, 276, 116, 62, 49, 141, 368, 147, 337, 33, 87,...
## $ `5/26/20` <dbl> 0, 189, 277, 122, 66, 51, 167, 380, 150, 338, 33, 90,...
## $ `5/27/20` <dbl> 0, 192, 281, 130, 71, 53, 176, 391, 152, 340, 33, 93,...
## $ `5/28/20` <dbl> 0, 205, 281, 132, 71, 58, 185, 392, 152, 349, 34, 97,...
## $ `5/29/20` <dbl> 0, 212, 282, 147, 71, 60, 201, 396, 153, 352, 36, 99,...
## $ `5/30/20` <dbl> 0, 216, 283, 150, 72, 61, 203, 402, 154, 353, 37, 100...
## $ `5/31/20` <dbl> 0, 220, 288, 164, 75, 62, 209, 410, 157, 355, 37, 100...
## $ `6/1/20` <dbl> 0, 233, 292, 172, 76, 63, 209, 414, 164, 358, 38, 103...
## $ `6/2/20` <dbl> 0, 238, 292, 175, 76, 63, 212, 416, 165, 358, 38, 104...
## $ `6/3/20` <dbl> 0, 239, 292, 177, 76, 63, 215, 419, 165, 359, 38, 105...
## $ `6/4/20` <dbl> 0, 241, 293, 177, 76, 63, 217, 421, 167, 360, 38, 107...
## $ `6/5/20` <dbl> 0, 248, 296, 183, 76, 64, 219, 431, 169, 363, 38, 108...
## $ `6/6/20` <dbl> 0, 259, 304, 190, 77, 70, 225, 442, 174, 373, 40, 108...
## $ `6/7/20` <dbl> 0, 265, 313, 193, 77, 72, 232, 449, 176, 378, 42, 110...
## $ `6/8/20` <dbl> 0, 272, 320, 197, 79, 73, 238, 455, 178, 383, 42, 111...
## $ `6/9/20` <dbl> 0, 282, 325, 199, 85, 75, 243, 464, 180, 391, 42, 117...
## $ `6/10/20` <dbl> 0, 295, 331, 208, 89, 79, 248, 471, 182, 401, 42, 118...
## $ `6/11/20` <dbl> 0, 312, 343, 214, 93, 87, 253, 484, 184, 417, 42, 121...
## $ `6/12/20` <dbl> 0, 323, 353, 221, 97, 95, 258, 499, 188, 427, 46, 122...
## $ `6/13/20` <dbl> 0, 331, 361, 226, 100, 102, 276, 517, 190, 438, 47, 1...
## $ `6/14/20` <dbl> 0, 357, 364, 234, 104, 110, 302, 536, 195, 453, 51, 1...
## $ `6/15/20` <dbl> 0, 368, 383, 238, 111, 116, 307, 544, 204, 475, 53, 1...
## $ `6/16/20` <dbl> 0, 373, 389, 245, 116, 121, 310, 551, 206, 485, 53, 1...
## $ `6/17/20` <dbl> 0, 375, 392, 251, 118, 123, 313, 554, 208, 486, 53, 1...
## $ `6/18/20` <dbl> 0, 400, 401, 263, 121, 130, 320, 566, 210, 501, 55, 1...
## $ `6/19/20` <dbl> 0, 411, 413, 266, 126, 139, 320, 569, 210, 507, 58, 1...
## $ `6/20/20` <dbl> 0, 431, 420, 272, 126, 143, 327, 572, 211, 516, 58, 1...
## $ `6/21/20` <dbl> 0, 434, 430, 272, 127, 149, 327, 576, 213, 521, 58, 1...
## $ `6/22/20` <dbl> 0, 442, 437, 277, 129, 153, 328, 578, 215, 528, 58, 1...
## $ `6/23/20` <dbl> 0, 453, 450, 280, 135, 159, 329, 581, 216, 534, 58, 1...
## $ `6/24/20` <dbl> 0, 469, 464, 288, 141, 168, 336, 584, 220, 543, 58, 1...
## $ `6/25/20` <dbl> 0, 479, 477, 305, 149, 176, 351, 588, 233, 549, 64, 1...
## $ `6/26/20` <dbl> 0, 488, 515, 312, 153, 184, 351, 594, 236, 559, 68, 1...
## $ `6/27/20` <dbl> 0, 498, 555, 317, 161, 188, 358, 600, 245, 561, 69, 2...
## $ `6/28/20` <dbl> 0, 503, 575, 317, 162, 189, 358, 602, 245, 561, 70, 2...
## $ `6/29/20` <dbl> 0, 527, 643, 322, 165, 199, 365, 605, 269, 585, 73, 2...
## $ `6/30/20` <dbl> 0, 537, 680, 325, 170, 208, 365, 607, 276, 590, 74, 2...
## $ `7/1/20` <dbl> 0, 553, 703, 326, 174, 218, 367, 607, 278, 595, 77, 2...
## $ `7/2/20` <dbl> 0, 561, 751, 335, 179, 222, 369, 610, 288, 611, 82, 2...
## $ `7/3/20` <dbl> 0, 568, 845, 348, 189, 230, 372, 625, 330, 625, 88, 2...
## $ `7/4/20` <dbl> 0, 591, 863, 350, 190, 234, 373, 626, 340, 637, 88, 2...
## $ `7/5/20` <dbl> 0, 615, 881, 352, 193, 239, 373, 634, 362, 642, 100, ...
## $ `7/6/20` <dbl> 0, 618, 911, 356, 197, 247, 373, 634, 384, 655, 105, ...
## $ `7/7/20` <dbl> 0, 644, 997, 360, 199, 255, 373, 634, 395, 656, 106, ...
## $ `7/8/20` <dbl> 0, 651, 1056, 366, 201, 262, 374, 639, 411, 660, 114,...
## $ `7/9/20` <dbl> 0, 661, 1131, 371, 211, 282, 375, 646, 445, 672, 115,...
## $ `7/10/20` <dbl> 0, 670, 1187, 381, 218, 292, 381, 648, 465, 679, 118,...
## $ `7/11/20` <dbl> 0, 684, 1224, 398, 224, 307, 382, 654, 500, 690, 128,...
## $ `7/12/20` <dbl> 0, 706, 1294, 403, 228, 331, 383, 655, 526, 693, 129,...
## $ `7/13/20` <dbl> 0, 728, 1359, 413, 231, 350, 383, 660, 566, 702, 136,...
## $ `7/14/20` <dbl> 0, 746, 1414, 428, 236, 366, 385, 661, 589, 712, 140,...
## $ `7/15/20` <dbl> 0, 756, 1518, 441, 242, 389, 386, 664, 655, 718, 145,...
## $ `7/16/20` <dbl> 0, 780, 1599, 459, 247, 424, 389, 669, 675, 731, 152,...
## $ `7/17/20` <dbl> 0, 789, 1689, 463, 255, 440, 393, 672, 720, 742, 157,...
## $ `7/18/20` <dbl> 0, 827, 1819, 483, 264, 458, 397, 678, 741, 756, 165,...
## $ `7/19/20` <dbl> 0, 842, 1937, 495, 269, 482, 398, 686, 785, 762, 173,...
## $ `7/20/20` <dbl> 0, 857, 2013, 503, 279, 507, 400, 689, 832, 767, 179,...
## $ `7/21/20` <dbl> 0, 865, 2102, 514, 283, 524, 401, 695, 869, 774, 182,...
## $ `7/22/20` <dbl> 0, 886, 2196, 518, 287, 547, 407, 701, 891, 782, 184,...
## $ `7/23/20` <dbl> 0, 905, 2461, 534, 289, 585, 408, 706, 934, 789, 193,...
## $ `7/24/20` <dbl> 0, 921, 2513, 539, 303, 615, 411, 711, 999, 797, 205,...
## $ `7/25/20` <dbl> 0, 932, 2662, 552, 318, 637, 414, 720, 1062, 810, 207...
## $ `7/26/20` <dbl> 0, 942, 2708, 562, 324, 646, 415, 724, 1113, 821, 209...
## $ `7/27/20` <dbl> 0, 965, 2770, 569, 334, 669, 416, 730, 1194, 825, 220...
## $ `7/28/20` <dbl> 0, 974, 2835, 575, 337, 675, 429, 734, 1243, 836, 221...
## $ `7/29/20` <dbl> 0, 974, 2835, 575, 338, 675, 429, 734, 1244, 836, 221...
## $ `7/30/20` <dbl> 0, 1002, 3028, 585, 352, 731, 435, 747, 1336, 848, 23...
## $ `7/31/20` <dbl> 0, 1015, 3101, 598, 363, 767, 437, 753, 1450, 859, 23...
## $ `8/1/20` <dbl> 0, 1030, 3142, 602, 368, 792, 443, 757, 1480, 861, 24...
## $ `8/2/20` <dbl> 0, 1052, 3223, 610, 372, 813, 445, 765, 1580, 868, 25...
## $ `8/3/20` <dbl> 0, 1066, 3265, 612, 382, 830, 446, 766, 1612, 875, 26...
## $ `8/4/20` <dbl> 0, 1073, 3320, 614, 389, 836, 449, 766, 1646, 882, 26...
## $ `8/5/20` <dbl> 0, 1073, 3380, 615, 392, 839, 452, 769, 1683, 886, 27...
## $ `8/6/20` <dbl> 0, 1096, 3438, 619, 421, 874, 458, 771, 1741, 893, 28...
## $ `8/7/20` <dbl> 0, 1113, 3504, 624, 424, 909, 462, 774, 1777, 899, 29...
## $ `8/8/20` <dbl> 0, 1134, 3564, 628, 434, 923, 471, 773, 1836, 904, 29...
## $ `8/9/20` <dbl> 0, 1215, 3606, 630, 446, 934, 472, 779, 1860, 906, 30...
## $ `8/10/20` <dbl> 0, 1215, 3714, 631, 450, 947, 474, 782, 1883, 909, 30...
## $ `8/11/20` <dbl> 0, 1215, 3736, 643, 455, 958, 489, 785, 1914, 916, 30...
## $ `8/12/20` <dbl> 0, 1241, 3776, 646, 464, 967, 500, 788, 1935, 918, 31...
## $ `8/13/20` <dbl> 0, 1250, 3813, 651, 469, 977, 501, 790, 1959, 919, 32...
## $ `8/14/20` <dbl> 0, 1252, 3860, 656, 477, 989, 502, 796, 1975, 922, 32...
## $ `8/15/20` <dbl> 0, 1262, 3909, 663, 483, 996, 503, 807, 2019, 925, 33...
## $ `8/16/20` <dbl> 0, 1273, 3948, 671, 483, 1005, 504, 811, 2037, 927, 3...
## $ `8/17/20` <dbl> 0, 1274, 3960, 672, 488, 1008, 504, 814, 2055, 928, 3...
## $ `8/18/20` <dbl> 0, 1291, 3977, 674, 490, 1034, 512, 814, 2107, 937, 3...
## $ `8/19/20` <dbl> 0, 1293, 4002, 683, 503, 1049, 530, 814, 2159, 941, 3...
## $ `8/20/20` <dbl> 0, 1293, 4035, 690, 507, 1077, 534, 814, 2214, 949, 3...
## $ `8/21/20` <dbl> 0, 1293, 4054, 690, 509, 1083, 534, 814, 2228, 952, 3...
## $ `8/22/20` <dbl> 0, 1322, 4115, 699, 516, 1096, 536, 822, 2276, 957, 3...
## $ `8/23/20` <dbl> 0, 1324, 4147, 702, 523, 1099, 536, 824, 2286, 958, 3...
## $ `8/24/20` <dbl> 0, 1351, 4167, 720, 526, 1135, 536, 825, 2327, 971, 3...
## $ `8/25/20` <dbl> 0, 1355, 4190, 724, 527, 1160, 536, 826, 2345, 973, 3...
## $ `8/26/20` <dbl> 0, 1366, 4265, 732, 530, 1195, 537, 833, 2400, 983, 3...
## $ `8/27/20` <dbl> 0, 1377, 4311, 739, 533, 1213, 538, 839, 2413, 1011, ...
## $ `8/28/20` <dbl> 0, 1389, 4347, 745, 535, 1219, 541, 840, 2443, 1017, ...
## $ `8/29/20` <dbl> 0, 1400, 4424, 753, 540, 1248, 546, 855, 2499, 1024, ...
## $ `8/30/20` <dbl> 0, 1438, 4525, 757, 550, 1277, 550, 864, 2533, 1027, ...
## $ `8/31/20` <dbl> 0, 1442, 4545, 757, 554, 1287, 551, 866, 2567, 1033, ...
## $ `9/1/20` <dbl> 0, 1452, 4568, 764, 558, 1303, 559, 871, 2619, 1041, ...
## $ `9/2/20` <dbl> 0, 1452, 4583, 768, 562, 1308, 561, 872, 2633, 1045, ...
## $ `9/3/20` <dbl> 0, 1466, 4628, 771, 564, 1336, 563, 874, 2678, 1046, ...
## $ `9/4/20` <dbl> 0, 1475, 4654, 776, 570, 1361, 563, 881, 2747, 1054, ...
## $ `9/5/20` <dbl> 0, 1492, 4686, 776, 576, 1376, 566, 886, 2830, 1059, ...
## $ `9/6/20` <dbl> 0, 1498, 4713, 777, 581, 1379, 568, 890, 2842, 1061, ...
## $ `9/7/20` <dbl> 0, 1504, 4730, 778, 583, 1384, 568, 892, 2877, 1063, ...
## $ `9/8/20` <dbl> 0, 1508, 4757, 778, 589, 1390, 568, 892, 2891, 1064, ...
## $ `9/9/20` <dbl> 0, 1522, 4787, 778, 591, 1401, 570, 895, 2907, 1068, ...
## $ `9/10/20` <dbl> 0, 1544, 4833, 785, 594, 1430, 572, 896, 2958, 1076, ...
## $ `9/11/20` <dbl> 0, 1551, 4886, 786, 602, 1441, 573, 896, 2988, 1088, ...
## $ `9/12/20` <dbl> 0, 1565, 4922, 792, 604, 1446, 574, 898, 3047, 1094, ...
## $ `9/13/20` <dbl> 0, 1576, 4959, 794, 607, 1453, 580, 899, 3093, 1094, ...
## $ `9/14/20` <dbl> 0, 1585, 4978, 801, 610, 1464, 580, 900, 3110, 1097, ...
## $ `9/15/20` <dbl> 0, 1601, 4992, 806, 611, 1475, 581, 901, 3127, 1102, ...
## $ `9/16/20` <dbl> 0, 1619, 5003, 809, 612, 1487, 583, 901, 3165, 1106, ...
## $ `9/17/20` <dbl> 0, 1624, 5021, 809, 617, 1504, 585, 902, 3211, 1106, ...
## $ `9/18/20` <dbl> 0, 1664, 5033, 824, 619, 1527, 585, 906, 3249, 1117, ...
## $ `9/19/20` <dbl> 0, 1673, 5047, 830, 628, 1542, 585, 908, 3320, 1123, ...
## $ `9/20/20` <dbl> 0, 1690, 5061, 835, 632, 1551, 587, 909, 3338, 1130, ...
## $ `9/21/20` <dbl> 0, 1691, 5087, 838, 635, 1560, 591, 911, 3374, 1132, ...
## $ `9/22/20` <dbl> 0, 1714, 5124, 848, 635, 1573, 593, 911, 3390, 1140, ...
## $ `9/23/20` <dbl> 0, 1715, 5141, 851, 638, 1580, 597, 911, 3401, 1144, ...
## $ `9/24/20` <dbl> 1074, 1715, 5141, 851, 638, 1580, 597, 911, 3401, 114...
## $ `9/25/20` <dbl> 0, 1757, 5456, 873, 652, 1608, 599, 912, 3499, 1161, ...
## $ `9/26/20` <dbl> 0, 1764, 5477, 882, 654, 1611, 604, 912, 3515, 1164, ...
## $ `9/27/20` <dbl> 0, 1773, 5526, 885, 656, 1617, 606, 913, 3534, 1168, ...
## $ `9/28/20` <dbl> 0, 1785, 5588, 886, 657, 1618, 607, 914, 3548, 1172, ...
## $ `9/29/20` <dbl> 0, 1787, 5606, 886, 658, 1621, 607, 917, 3556, 1175, ...
## $ `9/30/20` <dbl> 0, 1791, 5640, 896, 664, 1629, 610, 917, 3569, 1179, ...
## $ `10/1/20` <dbl> 0, 1798, 5997, 898, 672, 1634, 612, 919, 3587, 1181, ...
## Observations: 811,530
## Variables: 6
## $ countyFIPS <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ countyName <chr> "Statewide Unallocated", "Statewide Unallocated", "State...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "A...
## $ stateFIPS <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ date <date> 2020-01-22, 2020-01-23, 2020-01-24, 2020-01-25, 2020-01...
## $ cumCases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...## Parsed with column specification:
## cols(
## .default = col_double(),
## `County Name` = col_character(),
## State = col_character()
## )
## See spec(...) for full column specifications.## Observations: 3,195
## Variables: 258
## $ countyFIPS <dbl> 0, 1001, 1003, 1005, 1007, 1009, 1011, 1013, 1015, 10...
## $ `County Name` <chr> "Statewide Unallocated", "Autauga County", "Baldwin C...
## $ State <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL",...
## $ stateFIPS <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ `1/22/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/23/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/24/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/25/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/26/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/27/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/28/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/29/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/30/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `1/31/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/1/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/2/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/3/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/4/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/5/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/6/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/7/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/8/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/9/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/10/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/11/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/12/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/13/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/14/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/15/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/16/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/17/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/18/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/19/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/20/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/21/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/22/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/23/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/24/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/25/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/26/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/27/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/28/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `2/29/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/1/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/2/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/3/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/4/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/5/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/6/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/7/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/8/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/9/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/10/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/11/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/12/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/13/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/14/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/15/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/16/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/17/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/18/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/19/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/20/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/21/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/22/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/23/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/24/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/25/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/26/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/27/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/28/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/29/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/30/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `3/31/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `4/1/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `4/2/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `4/3/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `4/4/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `4/5/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `4/6/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `4/7/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `4/8/20` <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `4/9/20` <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ `4/10/20` <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 1,...
## $ `4/11/20` <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 1,...
## $ `4/12/20` <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 1,...
## $ `4/13/20` <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 1,...
## $ `4/14/20` <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 1,...
## $ `4/15/20` <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 1,...
## $ `4/16/20` <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 9, 0, 0, 0, 0, 0, 0, 0, 1,...
## $ `4/17/20` <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 1, 11, 0, 0, 0, 0, 0, 0, 0, 1...
## $ `4/18/20` <dbl> 0, 1, 1, 0, 0, 0, 0, 0, 2, 11, 0, 0, 0, 0, 0, 0, 0, 1...
## $ `4/19/20` <dbl> 0, 1, 1, 0, 0, 0, 0, 0, 2, 11, 0, 0, 0, 0, 0, 0, 0, 1...
## $ `4/20/20` <dbl> 0, 1, 1, 0, 0, 0, 0, 0, 2, 11, 0, 0, 0, 0, 0, 0, 0, 1...
## $ `4/21/20` <dbl> 0, 1, 2, 0, 0, 0, 0, 0, 3, 13, 0, 0, 0, 1, 0, 0, 0, 1...
## $ `4/22/20` <dbl> 0, 1, 2, 0, 0, 0, 0, 0, 3, 16, 0, 0, 0, 1, 0, 1, 0, 1...
## $ `4/23/20` <dbl> 0, 2, 2, 0, 0, 0, 0, 0, 3, 16, 0, 1, 0, 1, 1, 1, 0, 1...
## $ `4/24/20` <dbl> 0, 2, 2, 0, 0, 0, 0, 0, 3, 17, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `4/25/20` <dbl> 0, 2, 2, 0, 0, 0, 0, 0, 3, 18, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `4/26/20` <dbl> 0, 2, 2, 0, 0, 0, 0, 1, 3, 18, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `4/27/20` <dbl> 0, 3, 2, 0, 0, 0, 0, 1, 3, 18, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `4/28/20` <dbl> 0, 3, 2, 0, 0, 0, 0, 1, 3, 19, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `4/29/20` <dbl> 0, 3, 2, 1, 0, 0, 0, 1, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `4/30/20` <dbl> 0, 3, 3, 1, 0, 0, 0, 1, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/1/20` <dbl> 0, 3, 4, 1, 0, 0, 0, 1, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/2/20` <dbl> 0, 3, 4, 1, 0, 0, 0, 1, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/3/20` <dbl> 0, 3, 4, 1, 0, 0, 0, 1, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/4/20` <dbl> 0, 3, 4, 1, 0, 0, 0, 1, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/5/20` <dbl> 0, 3, 5, 1, 0, 0, 0, 2, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/6/20` <dbl> 0, 3, 5, 1, 0, 0, 1, 2, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/7/20` <dbl> 0, 3, 5, 1, 0, 0, 1, 2, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/8/20` <dbl> 0, 3, 5, 1, 1, 0, 1, 3, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/9/20` <dbl> 0, 3, 5, 1, 1, 0, 1, 6, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/10/20` <dbl> 0, 3, 5, 1, 1, 0, 1, 6, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/11/20` <dbl> 0, 3, 6, 1, 1, 0, 1, 6, 3, 21, 0, 1, 0, 1, 1, 1, 0, 2...
## $ `5/12/20` <dbl> 0, 3, 7, 1, 1, 0, 1, 6, 3, 21, 0, 1, 1, 1, 1, 1, 1, 2...
## $ `5/13/20` <dbl> 0, 3, 7, 1, 1, 0, 1, 6, 3, 22, 0, 1, 2, 1, 1, 1, 1, 2...
## $ `5/14/20` <dbl> 0, 3, 8, 1, 1, 0, 1, 8, 3, 22, 0, 1, 3, 2, 2, 1, 1, 2...
## $ `5/15/20` <dbl> 0, 3, 8, 1, 1, 0, 1, 9, 3, 22, 0, 1, 3, 2, 2, 1, 1, 2...
## $ `5/16/20` <dbl> 0, 3, 8, 1, 1, 0, 1, 9, 3, 22, 0, 1, 3, 2, 2, 1, 1, 2...
## $ `5/17/20` <dbl> 0, 3, 8, 1, 1, 1, 1, 9, 3, 22, 0, 1, 3, 2, 2, 1, 1, 2...
## $ `5/18/20` <dbl> 0, 3, 8, 1, 1, 1, 1, 10, 3, 22, 0, 1, 3, 2, 2, 1, 1, ...
## $ `5/19/20` <dbl> 0, 3, 8, 1, 1, 1, 1, 10, 3, 22, 0, 1, 3, 2, 2, 1, 1, ...
## $ `5/20/20` <dbl> 0, 3, 8, 1, 1, 1, 1, 11, 3, 23, 0, 1, 3, 2, 2, 1, 1, ...
## $ `5/21/20` <dbl> 0, 3, 8, 1, 1, 1, 1, 11, 3, 23, 0, 1, 3, 2, 2, 1, 1, ...
## $ `5/22/20` <dbl> 0, 3, 9, 1, 1, 1, 1, 11, 3, 23, 2, 1, 4, 2, 2, 1, 1, ...
## $ `5/23/20` <dbl> 0, 3, 9, 1, 1, 1, 1, 11, 3, 23, 2, 1, 4, 2, 2, 1, 1, ...
## $ `5/24/20` <dbl> 0, 3, 9, 1, 1, 1, 1, 12, 3, 23, 2, 1, 4, 2, 2, 1, 1, ...
## $ `5/25/20` <dbl> 0, 3, 9, 1, 1, 1, 3, 12, 3, 24, 2, 1, 4, 2, 2, 1, 1, ...
## $ `5/26/20` <dbl> 0, 3, 9, 1, 1, 1, 3, 13, 3, 24, 2, 1, 7, 2, 2, 1, 1, ...
## $ `5/27/20` <dbl> 0, 3, 9, 1, 1, 1, 3, 13, 3, 24, 2, 1, 7, 2, 2, 1, 1, ...
## $ `5/28/20` <dbl> 0, 3, 9, 1, 1, 1, 4, 15, 3, 24, 2, 1, 8, 2, 2, 1, 1, ...
## $ `5/29/20` <dbl> 0, 3, 9, 1, 1, 1, 4, 16, 3, 24, 3, 1, 8, 2, 2, 1, 1, ...
## $ `5/30/20` <dbl> 0, 4, 9, 1, 1, 1, 4, 17, 3, 25, 3, 1, 8, 2, 2, 1, 1, ...
## $ `5/31/20` <dbl> 0, 4, 9, 1, 1, 1, 5, 18, 3, 25, 3, 1, 8, 2, 2, 1, 1, ...
## $ `6/1/20` <dbl> 0, 5, 9, 1, 1, 1, 6, 18, 3, 25, 3, 1, 10, 2, 2, 1, 1,...
## $ `6/2/20` <dbl> 0, 5, 9, 1, 1, 1, 6, 18, 3, 26, 3, 1, 10, 2, 2, 1, 1,...
## $ `6/3/20` <dbl> 0, 5, 9, 1, 1, 1, 6, 18, 3, 26, 3, 1, 10, 2, 2, 1, 1,...
## $ `6/4/20` <dbl> 0, 5, 9, 1, 1, 1, 6, 18, 3, 26, 3, 1, 10, 2, 2, 1, 1,...
## $ `6/5/20` <dbl> 0, 5, 9, 1, 1, 1, 7, 21, 3, 26, 3, 1, 10, 2, 2, 1, 1,...
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## $ `8/17/20` <dbl> 0, 23, 32, 6, 6, 5, 14, 37, 23, 38, 9, 12, 12, 10, 5,...
## $ `8/18/20` <dbl> 0, 23, 33, 6, 6, 5, 14, 37, 25, 38, 9, 12, 12, 10, 5,...
## $ `8/19/20` <dbl> 0, 23, 33, 7, 6, 5, 14, 37, 25, 38, 9, 12, 12, 10, 5,...
## $ `8/20/20` <dbl> 0, 23, 34, 7, 6, 5, 14, 37, 25, 38, 10, 12, 12, 10, 5...
## $ `8/21/20` <dbl> 0, 23, 35, 7, 6, 6, 14, 37, 25, 38, 10, 13, 12, 10, 5...
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## $ `8/30/20` <dbl> 0, 23, 40, 7, 7, 10, 14, 37, 35, 40, 12, 13, 12, 14, ...
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## $ `9/1/20` <dbl> 0, 24, 42, 7, 8, 11, 14, 37, 38, 40, 12, 13, 12, 14, ...
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## $ `9/5/20` <dbl> 0, 24, 46, 7, 9, 12, 14, 38, 38, 41, 12, 16, 12, 14, ...
## $ `9/6/20` <dbl> 0, 24, 46, 7, 9, 12, 14, 38, 38, 41, 12, 16, 12, 14, ...
## $ `9/7/20` <dbl> 0, 24, 46, 7, 9, 12, 14, 38, 38, 41, 12, 16, 12, 14, ...
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## $ `9/10/20` <dbl> 0, 24, 46, 7, 9, 12, 14, 38, 38, 41, 13, 16, 12, 14, ...
## $ `9/11/20` <dbl> 0, 24, 47, 7, 9, 12, 14, 38, 38, 41, 14, 17, 12, 15, ...
## $ `9/12/20` <dbl> 0, 24, 47, 7, 9, 13, 14, 38, 38, 42, 14, 18, 12, 16, ...
## $ `9/13/20` <dbl> 0, 24, 47, 7, 9, 13, 14, 38, 38, 42, 14, 18, 12, 16, ...
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## $ `9/15/20` <dbl> 0, 24, 47, 7, 9, 13, 14, 38, 38, 42, 15, 24, 12, 16, ...
## $ `9/16/20` <dbl> 0, 24, 47, 7, 9, 13, 14, 38, 38, 42, 15, 24, 12, 16, ...
## $ `9/17/20` <dbl> 0, 24, 48, 7, 9, 13, 14, 38, 39, 42, 15, 25, 12, 17, ...
## $ `9/18/20` <dbl> 0, 24, 48, 7, 10, 14, 14, 38, 39, 42, 15, 27, 12, 17,...
## $ `9/19/20` <dbl> 0, 24, 49, 7, 10, 14, 14, 38, 39, 42, 15, 27, 12, 17,...
## $ `9/20/20` <dbl> 0, 24, 49, 7, 10, 14, 14, 38, 39, 42, 15, 27, 12, 17,...
## $ `9/21/20` <dbl> 0, 24, 49, 7, 10, 14, 14, 38, 39, 42, 15, 27, 12, 17,...
## $ `9/22/20` <dbl> 0, 25, 49, 7, 10, 14, 14, 38, 40, 42, 15, 28, 12, 17,...
## $ `9/23/20` <dbl> 0, 25, 49, 7, 10, 15, 14, 39, 40, 42, 15, 30, 12, 17,...
## $ `9/24/20` <dbl> 0, 25, 50, 7, 10, 15, 14, 39, 42, 42, 13, 30, 12, 16,...
## $ `9/25/20` <dbl> 0, 25, 50, 7, 10, 15, 14, 39, 44, 42, 13, 30, 12, 16,...
## $ `9/26/20` <dbl> 0, 25, 50, 7, 10, 15, 14, 39, 44, 42, 13, 30, 12, 16,...
## $ `9/27/20` <dbl> 0, 25, 50, 7, 10, 15, 14, 39, 44, 42, 13, 30, 12, 16,...
## $ `9/28/20` <dbl> 0, 25, 50, 7, 10, 15, 14, 39, 44, 42, 13, 30, 12, 16,...
## $ `9/29/20` <dbl> 0, 27, 50, 7, 10, 15, 14, 40, 44, 42, 13, 30, 12, 16,...
## $ `9/30/20` <dbl> 0, 27, 52, 7, 10, 15, 15, 40, 44, 42, 13, 30, 12, 16,...
## $ `10/1/20` <dbl> 0, 28, 53, 7, 10, 15, 16, 40, 44, 42, 13, 30, 12, 16,...
## Observations: 811,530
## Variables: 6
## $ countyFIPS <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ countyName <chr> "Statewide Unallocated", "Statewide Unallocated", "State...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "A...
## $ stateFIPS <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ date <date> 2020-01-22, 2020-01-23, 2020-01-24, 2020-01-25, 2020-01...
## $ cumDeaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
# STEP 3: Create appropriately filtered data without creating new clusters
clust_20201003 <- prepClusterCounties(burdenFile=burden_20201003,
maxDate=maxDate_20201003,
minPop=25000,
createClusters=FALSE
)## # A tibble: 1 x 6
## cpm_mean_is0 dpm_mean_is0 cpm_mean_lt100 dpm_mean_lt100 cpm_mean_lt5000
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0 0.0214 0 0.179 0.0761
## # ... with 1 more variable: dpm_mean_lt5000 <dbl># STEP 4: Assess the existing clusters against the new data, including the cumulative plot
helperACC_county_20201003 <- helperAssessCountyClusters(vecCluster=clustVec_county_20200903_new,
dfPop=clust_20201003$countyFiltered,
dfBurden=clust_20201003$countyFiltered,
showCum=TRUE,
thruLabel="Sep 30, 2020",
plotsTogether=TRUE,
orderCluster=TRUE
)
##
## Recency is defined as 2020-09-01 through current
##
## Recency is defined as 2020-09-01 through current## Warning: Removed 1 rows containing missing values (geom_point).
# STEP 5: Add back clusters not used for analysis (code 999) and associated disease data
clusterStateData_20201003 <- helperMakeClusterStateData(helperACC_county_20201003,
dfBurden=clust_20201003$countyDailyPerCapita,
orderCluster=TRUE
)## Joining, by = "fipsCounty"## Joining, by = "fipsCounty"# STEP 6: Run an example state-level summary using several of the largest states
stateCountySummary(states=c("CA", "TX", "FL", "NY", "IL", "PA"),
df=changeOrderLabel(clusterStateData_20201003, grpVars="fipsCounty"),
keyDate=maxDate_20201003,
showQuadrants=TRUE,
showCumulative=TRUE,
facetCumulativeByState = TRUE,
showAllFactorLevels = TRUE
)
# STEP 7: Assess lags for largest counties and counties with highest death rates
# STEP 7a: Save data for top-100 counties by death rate (filtered to only counties of 100k+ population)
top100_dpm_20201003 <- exploreTopCounties(helperACC_county_20201003,
minDate="2020-03-01",
maxDate="2020-09-30",
minPop=100000,
nVar="dpm",
nKey=100,
plotData=FALSE
)##
## *** KEY COUNTIES INCLUDE: ***
## # A tibble: 100 x 5
## state cluster countyName dpm pop
## <chr> <fct> <chr> <dbl> <dbl>
## 1 36005 3 3 - Bronx (NY) 3492. 1418207
## 2 36081 3 3 - Queens (NY) 3218. 2253858
## 3 36047 3 3 - Kings (NY) 2863. 2559903
## 4 34013 3 3 - Essex (NJ) 2663. 798975
## 5 34031 3 3 - Passaic (NJ) 2499. 501826
## 6 34039 3 3 - Union (NJ) 2437. 556341
## 7 36085 3 3 - Richmond (NY) 2281. 476143
## 8 34017 3 3 - Hudson (NJ) 2256. 672391
## 9 34003 3 3 - Bergen (NJ) 2194. 932202
## 10 48061 3 3 - Cameron (TX) 2155. 423163
## # ... with 90 more rows
##
## The best lags are:
## # A tibble: 100 x 5
## countyName state cluster corr lag
## <chr> <chr> <fct> <dbl> <int>
## 1 3 - Hampden (MA) 25013 3 0.924 0
## 2 3 - San Juan (NM) 35045 3 0.836 0
## 3 4 - Baltimore cityMD) 24510 4 0.535 0
## 4 4 - Coconino (AZ) 04005 4 0.265 0
## 5 4 - Hendricks (IN) 18063 4 0.467 0
## 6 4 - Johnson (IN) 18081 4 0.527 0
## 7 4 - Marion (IN) 18097 4 0.544 0
## 8 4 - Montgomery (AL) 01101 4 0.761 0
## 9 4 - Montgomery (MD) 24031 4 0.895 0
## 10 4 - Oakland (MI) 26125 4 0.478 0
## # ... with 90 more rowsglimpse(top100_dpm_20201003)## Observations: 21,200
## Variables: 9
## Groups: countyName, state, cluster [100]
## $ countyName <chr> "1 - Anderson (SC)", "1 - Highlands (FL)", "1 - Webb (TX...
## $ state <chr> "45007", "12055", "48479", "34003", "36005", "48061", "2...
## $ cluster <fct> 1, 1, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,...
## $ date <date> 2020-03-01, 2020-03-01, 2020-03-01, 2020-03-01, 2020-03...
## $ cpm7 <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.000000...
## $ dpm7 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ corr <dbl> 0.9199305, 0.7872620, 0.8755114, 0.8252094, 0.9747650, 0...
## $ lag <int> 26, 28, 30, 9, 6, 11, 8, 7, 9, 0, 2, 8, 26, 8, 6, 8, 10,...
## $ cpmlag <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, NA, NA, NA, NA, N...# STEP 7b: Top-3 by population by cluster with plots
exploreTopCounties(helperACC_county_20201003,
minDate="2020-03-01",
maxDate="2020-09-30",
topNBy="cluster",
nVar="pop",
nKey=3,
plotData=TRUE
)##
## *** KEY COUNTIES INCLUDE: ***
## # A tibble: 15 x 5
## state cluster countyName dpm pop
## <chr> <fct> <chr> <dbl> <dbl>
## 1 06037 5 5 - Los Angeles (CA) 655. 10039107
## 2 17031 4 4 - Cook (IL) 1015. 5150233
## 3 48201 1 1 - Harris (TX) 551. 4713325
## 4 04013 4 4 - Maricopa (AZ) 756. 4485414
## 5 06073 2 2 - San Diego (CA) 235. 3338330
## 6 06059 1 1 - Orange (CA) 399. 3175692
## 7 12086 4 4 - Miami-Dade (FL) 1207. 2716940
## 8 48113 1 1 - Dallas (TX) 430. 2635516
## 9 36047 3 3 - Kings (NY) 2863. 2559903
## 10 36081 3 3 - Queens (NY) 3218. 2253858
## 11 53033 5 5 - King (WA) 337. 2252782
## 12 06085 2 2 - Santa Clara (CA) 167. 1927852
## 13 26163 3 3 - Wayne (MI) 1706. 1749343
## 14 06001 2 2 - Alameda (CA) 247. 1671329
## 15 39049 5 5 - Franklin (OH) 467. 1316756
##
## The best lags are:
## # A tibble: 15 x 5
## countyName state cluster corr lag
## <chr> <chr> <fct> <dbl> <int>
## 1 5 - Franklin (OH) 39049 5 0.218 0
## 2 5 - Los Angeles (CA) 06037 5 0.616 0
## 3 2 - San Diego (CA) 06073 2 0.646 2
## 4 5 - King (WA) 53033 5 0.198 2
## 5 4 - Cook (IL) 17031 4 0.836 5
## 6 3 - Kings (NY) 36047 3 0.965 8
## 7 3 - Queens (NY) 36081 3 0.941 8
## 8 1 - Dallas (TX) 48113 1 0.782 9
## 9 3 - Wayne (MI) 26163 3 0.844 10
## 10 1 - Harris (TX) 48201 1 0.740 13
## 11 4 - Maricopa (AZ) 04013 4 0.965 18
## 12 1 - Orange (CA) 06059 1 0.757 24
## 13 2 - Alameda (CA) 06001 2 0.727 24
## 14 2 - Santa Clara (CA) 06085 2 0.487 30
## 15 4 - Miami-Dade (FL) 12086 4 0.785 30## Warning: Removed 26 rows containing missing values (geom_path).## Warning: Removed 26 rows containing missing values (geom_path).
## # A tibble: 3,180 x 9
## # Groups: countyName, state, cluster [15]
## countyName state cluster date cpm7 dpm7 corr lag cpmlag
## <chr> <chr> <fct> <date> <dbl> <dbl> <dbl> <int> <dbl>
## 1 1 - Dallas (TX) 48113 1 2020-03-01 0 0 0.782 9 NA
## 2 1 - Harris (TX) 48201 1 2020-03-01 0 0 0.740 13 NA
## 3 1 - Orange (CA) 06059 1 2020-03-01 0.0900 0 0.757 24 NA
## 4 2 - Alameda (CA) 06001 2 2020-03-01 0.0855 0 0.727 24 NA
## 5 2 - San Diego (CA) 06073 2 2020-03-01 0 0 0.646 2 NA
## 6 2 - Santa Clara (CA) 06085 2 2020-03-01 1.26 0 0.487 30 NA
## 7 3 - Kings (NY) 36047 3 2020-03-01 0 0 0.965 8 NA
## 8 3 - Queens (NY) 36081 3 2020-03-01 0 0 0.941 8 NA
## 9 3 - Wayne (MI) 26163 3 2020-03-01 0 0 0.844 10 NA
## 10 4 - Cook (IL) 17031 4 2020-03-01 0.0555 0 0.836 5 NA
## # ... with 3,170 more rows# STEP 7c: Top-5 by dpm by cluster with plots
exploreTopCounties(helperACC_county_20201003,
minDate="2020-03-01",
maxDate="2020-09-30",
topNBy="cluster",
nVar="dpm",
nKey=5,
plotData=TRUE
)##
## *** KEY COUNTIES INCLUDE: ***
## # A tibble: 25 x 5
## state cluster countyName dpm pop
## <chr> <fct> <chr> <dbl> <dbl>
## 1 36005 3 3 - Bronx (NY) 3492. 1418207
## 2 36081 3 3 - Queens (NY) 3218. 2253858
## 3 36047 3 3 - Kings (NY) 2863. 2559903
## 4 34013 3 3 - Essex (NJ) 2663. 798975
## 5 34031 3 3 - Passaic (NJ) 2499. 501826
## 6 48215 4 4 - Hidalgo (TX) 1755. 868707
## 7 04027 4 4 - Yuma (AZ) 1614. 213787
## 8 22017 4 4 - Caddo (LA) 1503. 240204
## 9 44007 4 4 - Providence (RI) 1398. 638931
## 10 45041 4 4 - Florence (SC) 1381. 138293
## # ... with 15 more rows
##
## The best lags are:
## # A tibble: 25 x 5
## countyName state cluster corr lag
## <chr> <chr> <fct> <dbl> <int>
## 1 2 - Pulaski (AR) 05119 2 0.558 0
## 2 5 - Columbiana (OH) 39029 5 -0.0149 0
## 3 5 - Litchfield (CT) 09005 5 0.922 4
## 4 4 - Caddo (LA) 22017 4 0.210 5
## 5 3 - Bronx (NY) 36005 3 0.975 6
## 6 3 - Essex (NJ) 34013 3 0.876 7
## 7 3 - Kings (NY) 36047 3 0.965 8
## 8 3 - Queens (NY) 36081 3 0.941 8
## 9 4 - Florence (SC) 45041 4 0.856 8
## 10 3 - Passaic (NJ) 34031 3 0.810 9
## # ... with 15 more rows## Warning: Removed 158 rows containing missing values (geom_path).## Warning: Removed 158 rows containing missing values (geom_path).
## # A tibble: 5,300 x 9
## # Groups: countyName, state, cluster [25]
## countyName state cluster date cpm7 dpm7 corr lag cpmlag
## <chr> <chr> <fct> <date> <dbl> <dbl> <dbl> <int> <dbl>
## 1 1 - Anderson (SC) 45007 1 2020-03-01 0 0 0.920 26 NA
## 2 1 - Highlands (FL) 12055 1 2020-03-01 0 0 0.787 28 NA
## 3 1 - Marion (FL) 12083 1 2020-03-01 0 0 0.904 27 NA
## 4 1 - Newton (GA) 13217 1 2020-03-01 0 0 0.781 26 NA
## 5 1 - Webb (TX) 48479 1 2020-03-01 0 0 0.876 30 NA
## 6 2 - Cabarrus (NC) 37025 2 2020-03-01 0 0 0.420 20 NA
## 7 2 - DeKalb (IL) 17037 2 2020-03-01 0 0 0.428 15 NA
## 8 2 - Douglas (NE) 31055 2 2020-03-01 0 0 0.539 18 NA
## 9 2 - Pulaski (AR) 05119 2 2020-03-01 0 0 0.558 0 0
## 10 2 - Warren (KY) 21227 2 2020-03-01 0 0 0.596 16 NA
## # ... with 5,290 more rowsCDC All-Cause Deaths
The CDC maintain public data for all-cause deaths by week and jurisdiction, available at CDC Weekly Deaths by Jurisdiction.
These data are known to have a lag between death and reporting, and the CDC back-correct to report deaths at the time the death occurred even if the death is reported in following weeks. This means totals for recent weeks tend to run low (lag), and the CDC run a projection of the expected total number of deaths given the historical lag times. Per other analysts on the internet, there is currently significant supra-lag, with lag times much longer than historical averages causing CDC projected deaths for recent weeks to be low.
Key Functions (CDC All-Cause Deaths)
Functions for reading and analyzing CDC all-cause deaths data have been sourced above from Coronavirus_Statistics_Functions_v002.R.
Running Key Analyses (CDC All-Cause Deaths)
The CDC data can be loaded and analyzed:
# Read and process the CDC data
cdc20200923 <- readProcessCDC("Weekly_counts_of_deaths_by_jurisdiction_and_age_group_downloaded_20200923.csv",
weekThru=30
)## Observations: 174,311
## Variables: 11
## $ Jurisdiction <chr> "Alabama", "Alabama", "Alabama", "Alabama", "A...
## $ `Week Ending Date` <chr> "1/10/2015", "1/17/2015", "1/24/2015", "1/31/2...
## $ `State Abbreviation` <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL"...
## $ Year <int> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015...
## $ Week <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,...
## $ `Age Group` <chr> "25-44 years", "25-44 years", "25-44 years", "...
## $ `Number of Deaths` <dbl> 67, 49, 55, 59, 47, 59, 41, 47, 59, 57, 54, 50...
## $ `Time Period` <chr> "2015-2019", "2015-2019", "2015-2019", "2015-2...
## $ Type <chr> "Predicted (weighted)", "Predicted (weighted)"...
## $ Suppress <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ Note <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## Observations: 174,311
## Variables: 11
## $ Jurisdiction <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alabama",...
## $ weekEnding <date> 2015-01-10, 2015-01-17, 2015-01-24, 2015-01-31, 2015-...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", ...
## $ year <int> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, ...
## $ week <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,...
## $ age <chr> "25-44 years", "25-44 years", "25-44 years", "25-44 ye...
## $ deaths <dbl> 67, 49, 55, 59, 47, 59, 41, 47, 59, 57, 54, 50, 58, 42...
## $ period <chr> "2015-2019", "2015-2019", "2015-2019", "2015-2019", "2...
## $ type <chr> "Predicted (weighted)", "Predicted (weighted)", "Predi...
## $ Suppress <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ Note <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
##
## Check Control Levels and Record Counts for Renamed Data:
## # A tibble: 6 x 4
## age n n_deaths_na deaths
## <chr> <int> <int> <dbl>
## 1 25-44 years 26066 3 3170502
## 2 45-64 years 31884 7 12464677
## 3 65-74 years 31880 10 12347549
## 4 75-84 years 31902 11 15363924
## 5 85 years and older 31888 9 20067928
## 6 Under 25 years 20691 0 1372225
## # A tibble: 12 x 6
## # Groups: period, year [6]
## period year type n n_deaths_na deaths
## <chr> <int> <chr> <int> <int> <dbl>
## 1 2015-2019 2015 Predicted (weighted) 15285 0 5416393
## 2 2015-2019 2015 Unweighted 15285 0 5416393
## 3 2015-2019 2016 Predicted (weighted) 15365 0 5483764
## 4 2015-2019 2016 Unweighted 15365 0 5483764
## 5 2015-2019 2017 Predicted (weighted) 15318 0 5643350
## 6 2015-2019 2017 Unweighted 15318 0 5643350
## 7 2015-2019 2018 Predicted (weighted) 15305 0 5698002
## 8 2015-2019 2018 Unweighted 15305 0 5698002
## 9 2015-2019 2019 Predicted (weighted) 15319 0 5725516
## 10 2015-2019 2019 Unweighted 15319 0 5725516
## 11 2020 2020 Predicted (weighted) 10586 24 4476340
## 12 2020 2020 Unweighted 10541 16 4376415
## # A tibble: 3 x 5
## # Groups: period [2]
## period Suppress n n_deaths_na deaths
## <chr> <chr> <int> <int> <dbl>
## 1 2015-2019 <NA> 153184 0 5.59e7
## 2 2020 Suppressed (counts highly incomplete, <5~ 40 40 0.
## 3 2020 <NA> 21087 0 8.85e6
## # A tibble: 9 x 5
## # Groups: period [2]
## period Note n n_deaths_na deaths
## <chr> <chr> <int> <int> <dbl>
## 1 2015-20~ <NA> 153184 0 5.59e7
## 2 2020 Data in recent weeks are incomplete. Only~ 16591 0 7.27e6
## 3 2020 Data in recent weeks are incomplete. Only~ 324 0 1.52e5
## 4 2020 Data in recent weeks are incomplete. Only~ 288 30 3.05e4
## 5 2020 Data in recent weeks are incomplete. Only~ 1502 10 4.17e5
## 6 2020 Data in recent weeks are incomplete. Only~ 60 0 2.71e4
## 7 2020 Estimates for Pennsylvania are too low fo~ 48 0 2.23e4
## 8 2020 Weights may be too low to account for und~ 436 0 1.40e5
## 9 2020 <NA> 1878 0 7.97e5
## state Jurisdiction n n_deaths_na deaths
## 1 US United States 3552 0 32276762
## 2 CA California 3552 0 3072016
## 3 FL Florida 3552 0 2357528
## 4 TX Texas 3552 0 2314502
## 5 PA Pennsylvania 3552 0 1548716
## 6 OH Ohio 3552 0 1395014
## 7 IL Illinois 3552 0 1219119
## 8 NY New York 3552 0 1155790
## 9 MI Michigan 3552 0 1111203
## 10 NC North Carolina 3521 17 1051632
## 11 GA Georgia 3551 0 965504
## 12 NJ New Jersey 3546 0 867210
## 13 TN Tennessee 3552 0 840787
## 14 VA Virginia 3552 0 774383
## 15 IN Indiana 3550 0 749760
## 16 MO Missouri 3548 0 728220
## 17 MA Massachusetts 3516 0 685409
## 18 AZ Arizona 3552 0 684537
## 19 YC New York City 3548 0 671106
## 20 WA Washington 3551 0 645406
## 21 AL Alabama 3550 0 598526
## 22 WI Wisconsin 3533 0 592047
## 23 MD Maryland 3546 0 570238
## 24 SC South Carolina 3549 0 560415
## 25 KY Kentucky 3519 0 545032
## 26 LA Louisiana 3545 0 525668
## 27 MN Minnesota 3509 0 503567
## 28 CO Colorado 3550 0 446708
## 29 OK Oklahoma 3541 0 445362
## 30 OR Oregon 3382 0 413553
## 31 MS Mississippi 3488 0 363792
## 32 AR Arkansas 3444 0 361612
## 33 CT Connecticut 3106 13 356416
## 34 IA Iowa 3190 0 339791
## 35 PR Puerto Rico 3272 0 331654
## 36 KS Kansas 3246 0 296520
## 37 NV Nevada 3291 0 289275
## 38 WV West Virginia 3011 10 251046
## 39 UT Utah 3438 0 213931
## 40 NM New Mexico 3140 0 205026
## 41 NE Nebraska 2846 0 188492
## 42 ME Maine 2646 0 160341
## 43 ID Idaho 2766 0 152936
## 44 NH New Hampshire 2672 0 135110
## 45 HI Hawaii 2556 0 124379
## 46 RI Rhode Island 2474 0 114274
## 47 MT Montana 2556 0 109719
## 48 DE Delaware 2558 0 99625
## 49 SD South Dakota 2448 0 86461
## 50 ND North Dakota 2433 0 75079
## 51 DC District of Columbia 2545 0 64202
## 52 VT Vermont 2336 0 61925
## 53 WY Wyoming 2318 0 47230
## 54 AK Alaska 2352 0 42249
## Observations: 174,311
## Variables: 11
## $ Jurisdiction <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alabama",...
## $ weekEnding <date> 2015-01-10, 2015-01-17, 2015-01-24, 2015-01-31, 2015-...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", ...
## $ year <fct> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, ...
## $ week <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,...
## $ age <fct> 25-44 years, 25-44 years, 25-44 years, 25-44 years, 25...
## $ deaths <dbl> 67, 49, 55, 59, 47, 59, 41, 47, 59, 57, 54, 50, 58, 42...
## $ period <fct> 2015-2019, 2015-2019, 2015-2019, 2015-2019, 2015-2019,...
## $ type <chr> "Predicted (weighted)", "Predicted (weighted)", "Predi...
## $ Suppress <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ Note <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## Observations: 85,466
## Variables: 11
## $ Jurisdiction <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alabama",...
## $ weekEnding <date> 2015-01-10, 2015-01-17, 2015-01-24, 2015-01-31, 2015-...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", ...
## $ year <fct> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, ...
## $ week <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,...
## $ age <fct> 25-44 years, 25-44 years, 25-44 years, 25-44 years, 25...
## $ deaths <dbl> 67, 49, 55, 59, 47, 59, 41, 47, 59, 57, 54, 50, 58, 42...
## $ period <fct> 2015-2019, 2015-2019, 2015-2019, 2015-2019, 2015-2019,...
## $ type <chr> "Predicted (weighted)", "Predicted (weighted)", "Predi...
## $ Suppress <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ Note <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
##
## Confirm that data suppressions and NA deaths have been aliminated:
## # A tibble: 0 x 11
## # ... with 11 variables: Jurisdiction <chr>, weekEnding <date>, state <chr>,
## # year <fct>, week <int>, age <fct>, deaths <dbl>, period <fct>, type <chr>,
## # Suppress <chr>, Note <chr>
## Observations: 80,379
## Variables: 12
## $ Jurisdiction <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alabama",...
## $ weekEnding <date> 2015-01-10, 2015-01-10, 2015-01-10, 2015-01-10, 2015-...
## $ state <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", ...
## $ year <fct> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, ...
## $ week <int> 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, ...
## $ age <fct> Under 25 years, 25-44 years, 45-64 years, 65-74 years,...
## $ period <fct> 2015-2019, 2015-2019, 2015-2019, 2015-2019, 2015-2019,...
## $ type <chr> "Predicted (weighted)", "Predicted (weighted)", "Predi...
## $ Suppress <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ n <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
## $ deaths <dbl> 25, 67, 253, 202, 272, 320, 28, 49, 256, 222, 253, 332...
## $ Note <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
##
## First duplicate is in row number (0 means no duplicates): 0# Generate plots of the processed CDC data
cdcBasicPlots(cdc20200923, clustVec=test_hier5$useClusters)
# Example cohort analysis for age 65+
list_65plus <- cdcCohortAnalysis(cohortName="65+ years old",
df=cdc20200923,
critFilter=list("age"=c("65-74 years",
"75-84 years",
"85 years and older"
)
),
curYear=2020,
startYear=2015,
startWeek=9,
plotTitle="All-cause deaths for 65+ years old cohort",
predActualPlotsOnePage=TRUE
)
# Example cohort analysis for full US
list_allUS <- cdcCohortAnalysis(cohortName="all ages, all states",
df=cdc20200923,
curYear=2020,
startYear=2015,
startWeek=9,
plotTitle="All-cause US total deaths",
predActualPlotsOnePage=TRUE
)
# Example cohort analysis for handful of state hit early
list_early <- cdcCohortAnalysis(cohortName="all ages, NY/NJ/CT/MA",
df=cdc20200923,
critFilter=list("state"=c("NY", "NJ", "CT", "MA")),
curYear=2020,
startYear=2015,
startWeek=9,
plotTitle="All-cause total deaths for NY/NJ/CT/MA",
predActualPlotsOnePage=TRUE
)
Next steps are to continue with the CDC aggregates function (analyze by cluster or age or state ot etc.):
# Testing the aggregation function for cluster
# Need to fix plot chart titles
clusterList_hier5_201001 <- helperKeyStateClusterMetrics(test_hier5_201001)
clusterAgg_20200923 <- cdcAggregateSummary(df=cdc20200923,
critVar="state",
critSubsets=clusterList_hier5_201001$stateCluster,
startWeek=9,
critListNames=paste0("cluster ", 1:5),
factorCritList=FALSE,
popData=clusterList_hier5_201001$pop,
cvDeathData=clusterList_hier5_201001$deaths,
idVarName="cluster"
)
# Testing the aggregation function for state (no plots)
stateAgg_20200923 <- cdcAggregateSummary(df=cdc20200923,
critVar="state",
critSubsets=names(clusterList_hier5_201001$clData),
startWeek=9,
idVarName="state",
subListNames=names(clusterList_hier5_201001$clData),
showAllPlots=FALSE
)
# Create a mapping of epiweek to month (use 2020 for this)
epiMonth <- tibble::tibble(dt=as.Date("2020-01-01")+0:365,
month=lubridate::month(dt),
quarter=lubridate::quarter(dt),
ew=lubridate::epiweek(dt)
) %>%
count(ew, month, quarter) %>%
arrange(ew, -n) %>%
group_by(ew) %>%
summarize(month=factor(month.abb[first(month)], levels=month.abb), quarter=first(quarter))
# Create plots by state
helperKeyStateExcessPlots(df=stateAgg_20200923,
epiMonth=epiMonth,
cvDeaths=test_hier5_201001$consolidatedPlotData,
startWeek=10,
cvDeathDate=as.Date("2020-07-31"),
subT="CDC data through July 2020 (Q3 incomplete)"
)## Warning: Column `state` joining factor and character vector, coercing into
## character vector
## Joining, by = "state"
## Warning: Removed 1 rows containing missing values (position_stack).## Warning: Removed 1 rows containing missing values (geom_text).
## # A tibble: 357 x 7
## state quarter month postStart excess pop excesspm
## <chr> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 AK Q1-2020 Jan 0 16.3 738432 22.1
## 2 AK Q1-2020 Feb 0 9.85 738432 13.3
## 3 AK Q1-2020 Mar 1 -21.6 738432 -29.2
## 4 AK Q2-2020 Apr 1 -9.29 738432 -12.6
## 5 AK Q2-2020 May 1 28.0 738432 38.0
## 6 AK Q2-2020 Jun 1 16.4 738432 22.3
## 7 AK Q3-2020 Jul 1 5.85 738432 7.92
## 8 AL Q1-2020 Jan 0 -527. 4858979 -108.
## 9 AL Q1-2020 Feb 0 -84.7 4858979 -17.4
## 10 AL Q1-2020 Mar 1 -74.5 4858979 -15.3
## # ... with 347 more rows# Testing the aggregation function for age (no plots)
ageAgg_20200923 <- cdcAggregateSummary(df=cdc20200923,
critVar="age",
critSubsets=levels(cdc20200923$age),
startWeek=9,
idVarName="age",
subListNames=levels(cdc20200923$age),
showAllPlots=TRUE
)
# Estimated US population by age (2020)
usPopAge2020 <- survival::uspop2[, , "2020"] %>%
apply(1, FUN=sum) %>%
tibble::tibble(ageActual=as.integer(names(.)), pop_2020=.)
usPopAge2020## # A tibble: 101 x 2
## ageActual pop_2020
## <int> <int>
## 1 0 4583634
## 2 1 4575533
## 3 2 4567477
## 4 3 4561292
## 5 4 4557600
## 6 5 4555265
## 7 6 4552366
## 8 7 4548108
## 9 8 4542047
## 10 9 4534181
## # ... with 91 more rows# Sums by age bucket
usPopBucket2020 <- usPopAge2020 %>%
mutate(age=factor(case_when(ageActual <= 24 ~ "Under 25 years",
ageActual <= 44 ~ "25-44 years",
ageActual <= 64 ~ "45-64 years",
ageActual <= 74 ~ "65-74 years",
ageActual <= 84 ~ "75-84 years",
TRUE ~ "85 years and older"
), levels=levels(cdc20200923$age)
)
) %>%
group_by(age) %>%
summarize(pop=sum(pop_2020))
usPopBucket2020## # A tibble: 6 x 2
## age pop
## <fct> <int>
## 1 Under 25 years 112501697
## 2 25-44 years 89724301
## 3 45-64 years 84356197
## 4 65-74 years 32312186
## 5 75-84 years 15895265
## 6 85 years and older 6597019# Create plots by age
helperKeyAgeExcessPlots(df=ageAgg_20200923,
epiMonth=epiMonth,
cvDeaths=test_hier5_201001$consolidatedPlotData,
popData=usPopBucket2020,
startWeek=10,
cvDeathDate=as.Date("2020-07-31"),
subT="CDC data through July 2020 (Q3 incomplete)"
)
## # A tibble: 42 x 7
## age quarter month postStart excess pop excesspm
## <fct> <fct> <fct> <dbl> <dbl> <int> <dbl>
## 1 Under 25 years Q1-2020 Jan 0 249. 112501697 2.21
## 2 Under 25 years Q1-2020 Feb 0 113. 112501697 1.00
## 3 Under 25 years Q1-2020 Mar 1 -72.4 112501697 -0.644
## 4 Under 25 years Q2-2020 Apr 1 154. 112501697 1.37
## 5 Under 25 years Q2-2020 May 1 216. 112501697 1.92
## 6 Under 25 years Q2-2020 Jun 1 468. 112501697 4.16
## 7 Under 25 years Q3-2020 Jul 1 519. 112501697 4.61
## 8 25-44 years Q1-2020 Jan 0 337. 89724301 3.76
## 9 25-44 years Q1-2020 Feb 0 236. 89724301 2.63
## 10 25-44 years Q1-2020 Mar 1 851. 89724301 9.48
## # ... with 32 more rows# Create data at the level of elderly and non-elderly, using 65 as the start if elderly
ageOld <- c("65-74 years", "75-84 years", "85 years and older")
cdcTwoAge <- cdc20200923 %>%
mutate(ageSplit=factor(ifelse(age %in% ageOld, "65 and over", "Under 65"),
levels=c("Under 65", "65 and over")
)
) %>%
group_by(state, year, week, ageSplit) %>%
summarize(deaths=sum(deaths)) %>%
ungroup() %>%
mutate(keyVar=paste(ageSplit, state, sep="_"))
cdcTwoAge## # A tibble: 29,552 x 6
## state year week ageSplit deaths keyVar
## <chr> <fct> <int> <fct> <dbl> <chr>
## 1 AK 2015 1 Under 65 22 Under 65_AK
## 2 AK 2015 1 65 and over 40 65 and over_AK
## 3 AK 2015 2 Under 65 22 Under 65_AK
## 4 AK 2015 2 65 and over 55 65 and over_AK
## 5 AK 2015 3 Under 65 19 Under 65_AK
## 6 AK 2015 3 65 and over 53 65 and over_AK
## 7 AK 2015 4 Under 65 17 Under 65_AK
## 8 AK 2015 4 65 and over 51 65 and over_AK
## 9 AK 2015 5 Under 65 41 Under 65_AK
## 10 AK 2015 5 65 and over 33 65 and over_AK
## # ... with 29,542 more rows# Testing the aggregation function for state and elderly (no plots)
ageStateAgg_20200923 <- cdcAggregateSummary(df=cdcTwoAge,
critVar="keyVar",
critSubsets=unique(cdcTwoAge$keyVar),
startWeek=9,
idVarName="keyVar",
subListNames=unique(cdcTwoAge$keyVar),
showAllPlots=FALSE
) %>%
tidyr::separate(keyVar, into=c("age", "state"), sep="_")
# Plot of excess deaths ratio by state and age
ageStateAgg_20200923 %>%
filter(year==2020, week>=10) %>%
group_by(age, state) %>%
summarize(excess=sum(delta)) %>%
ggplot(aes(x=fct_reorder(state, excess, .fun=sum), y=excess/1000, fill=age)) +
geom_col(position="fill") +
coord_flip() +
labs(x="State",
y="% of All-cause excess deaths",
title="Proportion of all-cause excess deaths by age cohort in 2020",
subtitle="CDC data March 2020 through July 2020"
) +
scale_fill_discrete("Age Group") +
geom_hline(aes(yintercept=0.25), lty=2)
# Plots by age cohort
for (age in levels(cdcTwoAge$ageSplit)) {
cdcCohortAnalysis(df=cdcTwoAge,
cohortName=paste0("ages ", age),
critFilter=list("ageSplit"=age),
plotTitle=paste0("All-cause total deaths for age ", age),
showSubsetPlots=FALSE,
showPredActualPlots=TRUE,
predActualPlotsOnePage=TRUE
)
}
Next steps are to further clean up and integrate the functions so that the full process can be run with more recent data using fewer changes to the code.